LGBM_missing_values/LGBM_with_missing_values.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "15bdb8ad-0e7b-469b-b3a2-99d1caa9c7dc",
   "metadata": {},
   "source": [
    "# Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b4233b-c4c2-440e-9a76-14f2ec910268",
   "metadata": {},
   "source": [
    "This notebook answers: **how LGBM deals with missing values?** What does the algorithm do when there are missing values on the training dataset and on prediction time? \n",
    "\n",
    "To achieve this I tested the following scenarios: \n",
    "- if the missing values are on training data or only on prediction time\n",
    "- if, on training data, the NaN values reduce the loss or no\n",
    "- LGBMClassifier x LGBMRegressor\n",
    "\n",
    "The conclusion:\n",
    "\n",
    ">LGBM uses the NaN values as any other value, trying to use it on the tree if it reduces the loss.\n",
    ">When the NaN values aren't on the training dataset, the prediction for those values will be the same as the one of the leftmost leaves (default path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffa59b05-36dd-4cd0-86a7-0b00877b58eb",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68f055f4-9834-4076-aa10-2173749e554b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.special import expit\n",
    "import lightgbm as lgb\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49417ac-cb15-46a2-aef4-72ab8090ee05",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e9c8d599-023c-4b2a-b6ea-6b049f3b713a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_and_evaluate(df, lgbm_params, n_trees_to_plot=1, table_tree_index_to_display=None, cols_table_tree_to_filter=None):\n",
    "    model = lgb.LGBMClassifier(**lgbm_params)\n",
    "    model.fit(df[[\"X\"]],df[[\"y\"]])\n",
    "    \n",
    "    df[\"y_pred\"] = model.predict(df[[\"X\"]])\n",
    "    df[\"y_pred_proba\"] = model.predict_proba(df[[\"X\"]])[:, 1]\n",
    "    print(f'Accuracy is: {accuracy_score(df[\"y\"], df[\"y_pred\"])}')\n",
    "    display(\n",
    "        df\\\n",
    "        .groupby([\"X\", \"y\"], dropna=False)\\\n",
    "        .agg(y_pred=('y_pred', 'mean'), y_pred_proba=('y_pred_proba', 'mean'), count=('y_pred', 'count'))\\\n",
    "        .reset_index()\n",
    "    )\n",
    "\n",
    "    table_tree = model.booster_.trees_to_dataframe()\n",
    "\n",
    "    if table_tree_index_to_display is not None:\n",
    "        table_tree = table_tree\\\n",
    "            .query(\"tree_index == @table_tree_index_to_display\")\n",
    "    if cols_table_tree_to_filter is not None:\n",
    "        table_tree = table_tree[cols_table_tree_to_filter]\n",
    "    display(table_tree)\n",
    "\n",
    "    n_trees_to_plot = model.booster_.num_trees() if n_trees_to_plot is None else n_trees_to_plot\n",
    "    iterations = min(n_trees_to_plot, model.booster_.num_trees())\n",
    "    for tree_index in range(0, n_trees_to_plot):\n",
    "        display(lgb.plot_tree(model, example_case=pd.DataFrame([{\"X\": np.NaN}]), show_info=\"data_percentage\", tree_index=tree_index))\n",
    "    return model\n",
    "\n",
    "def predict_and_evaluate_classifier(df, features, y_col, model):\n",
    "    df[\"y_pred\"] = model.predict(df[features])\n",
    "    df[\"y_pred_proba\"] = model.predict_proba(df[features])[:, 1]\n",
    "    print(f'Accuracy is: {accuracy_score(df[y_col], df[\"y_pred\"])}')\n",
    "    return df\\\n",
    "        .groupby(features + [y_col], dropna=False)\\\n",
    "        .agg(y_pred=('y_pred', 'mean'), y_pred_proba=('y_pred_proba', 'mean'), count=('y_pred', 'count'))\\\n",
    "        .reset_index()\n",
    "\n",
    "def predict_and_evaluate_regressor(df, features, y_col, model):\n",
    "    df[\"y_pred\"] = model.predict(df[features])\n",
    "    print(f'RMSe is: {mean_squared_error(df[y_col], df[\"y_pred\"], squared=False)}')\n",
    "    return df\\\n",
    "        .groupby(features + [y_col], dropna=False)\\\n",
    "        .agg(y_pred=('y_pred', 'mean'), count=('y_pred', 'count'))\\\n",
    "        .reset_index()\n",
    "    \n",
    "def trees_to_dataframe(df, tree_indexes_to_filter=None, cols_to_filter=[\"node_depth\", \"node_index\", \"split_gain\", \"threshold\", \"decision_type\", \"missing_direction\", \"value\",\"count\"]):\n",
    "    table_tree = model.booster_.trees_to_dataframe()\n",
    "\n",
    "    if tree_indexes_to_filter is not None:\n",
    "        table_tree = table_tree\\\n",
    "            .query(\"tree_index == @tree_indexes_to_filter\")\n",
    "    if cols_to_filter is not None:\n",
    "        table_tree = table_tree[cols_to_filter]\n",
    "    return table_tree\n",
    "\n",
    "def plot_tree_on_diagram(model, n_trees_to_plot=1):\n",
    "    n_trees_to_plot = model.booster_.num_trees() if n_trees_to_plot is None else n_trees_to_plot\n",
    "    iterations = min(n_trees_to_plot, model.booster_.num_trees())\n",
    "    for tree_index in range(0, n_trees_to_plot):\n",
    "        display(lgb.plot_tree(model, example_case=pd.DataFrame([{\"X\": np.NaN}]), show_info=\"data_percentage\", tree_index=tree_index))\n",
    "    return model\n",
    "\n",
    "def train_lgbm_model(x, y, lgbm_class, lgbm_params):\n",
    "    model = lgbm_class(**lgbm_params)\n",
    "    model.fit(x, y)\n",
    "    return model\n",
    "    \n",
    "def predict_missing_value(model):\n",
    "    test_df = pd.DataFrame([{\"X\": np.NaN}])\n",
    "    test_df[\"y_pred\"] = model.predict(test_df[[\"X\"]])\n",
    "    return test_df\n",
    "\n",
    "def plot_interactive_tree(model, tree_index=None):\n",
    "    if tree_index is None:\n",
    "        tree_index=model.booster_.num_trees() - 1\n",
    "    try:\n",
    "        # To enable interactive mode you should install ipywidgets\n",
    "        # https://github.com/jupyter-widgets/ipywidgets\n",
    "        from ipywidgets import interact, SelectMultiple\n",
    "        INTERACTIVE = True\n",
    "    except ImportError:\n",
    "        INTERACTIVE = False\n",
    "    \n",
    "    def render_tree(tree_index, show_info, precision=3):\n",
    "        show_info = None if 'None' in show_info else show_info\n",
    "        return lgb.create_tree_digraph(model, tree_index=tree_index,\n",
    "                                       show_info=show_info, precision=precision)\n",
    "\n",
    "    \n",
    "    if INTERACTIVE:\n",
    "        # create widget to switch between trees and control info in nodes\n",
    "        interact(render_tree,\n",
    "                 tree_index=(0, tree_index),\n",
    "                 show_info=SelectMultiple(  # allow multiple values to be selected\n",
    "                     options=['None',\n",
    "                              'split_gain',\n",
    "                              'internal_value',\n",
    "                              'internal_count',\n",
    "                              'internal_weight',\n",
    "                              'leaf_count',\n",
    "                              'leaf_weight',\n",
    "                              'data_percentage'],\n",
    "                     value=['None']),\n",
    "                 precision=(0, 10))\n",
    "        tree = None\n",
    "    else:\n",
    "        tree = render_tree(tree_index, ['None'])\n",
    "    return tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1e874f-0b2c-4764-88f8-cab1f6305ddd",
   "metadata": {},
   "source": [
    "# LGBM Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc71b7d1-3715-4212-96f7-4cdb85876398",
   "metadata": {},
   "source": [
    "## NaN on training dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c1717d0-94ec-4596-adf6-3cc31c01f692",
   "metadata": {},
   "source": [
    "### NaN reduce loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "055f0f7c-8523-481f-a2f1-dcdba88f9bd5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
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       "      <th>X</th>\n",
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       "      <th>y_pred</th>\n",
       "      <th>y_pred_proba</th>\n",
       "      <th>count</th>\n",
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      "text/plain": [
       "     X  y  y_pred  y_pred_proba  count\n",
       "0  1.0  0     0.0      0.000015    333\n",
       "1  2.0  0     0.0      0.000015    333\n",
       "2  NaN  1     1.0      0.999971    333"
      ]
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       "      <th></th>\n",
       "      <th>node_depth</th>\n",
       "      <th>node_index</th>\n",
       "      <th>split_gain</th>\n",
       "      <th>threshold</th>\n",
       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.843147</td>\n",
       "      <td>666</td>\n",
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      "text/plain": [
       "   node_depth node_index  split_gain     threshold decision_type  \\\n",
       "0           1       0-S0       999.0  1.000000e-35            <=   \n",
       "1           2       0-L0         NaN           NaN          None   \n",
       "2           2       0-L1         NaN           NaN          None   \n",
       "\n",
       "  missing_direction     value  count  \n",
       "0              left -0.693147    999  \n",
       "1              None -0.393147    333  \n",
       "2              None -0.843147    666  "
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      "text/plain": [
       "LGBMClassifier(verbose=-1)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
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     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([1, 2, np.NaN], N / 3),\n",
    "    'y': np.repeat([0, 0, 1], N / 3)\n",
    "\n",
    "})\n",
    "lgbm_params = {\"verbose\": -1}\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMClassifier, lgbm_params)\n",
    "display(predict_and_evaluate_classifier(df, [\"X\"], \"y\", model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a2565b-b453-4595-95a1-91eb2a4ed450",
   "metadata": {},
   "source": [
    "### NaN don't reduce loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "db3a4a0e-1d5e-4f31-aeed-195aba5d374e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>y_pred_proba</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.995183</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004817</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500003</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500003</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X  y  y_pred  y_pred_proba  count\n",
       "0  0.0  1     1.0      0.995183    250\n",
       "1  1.0  0     0.0      0.004817    250\n",
       "2  NaN  0     1.0      0.500003    250\n",
       "3  NaN  1     1.0      0.500003    250"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_depth</th>\n",
       "      <th>node_index</th>\n",
       "      <th>split_gain</th>\n",
       "      <th>threshold</th>\n",
       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0-S0</td>\n",
       "      <td>333.333008</td>\n",
       "      <td>1.000000e-35</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0-S1</td>\n",
       "      <td>166.667007</td>\n",
       "      <td>1.000000e-35</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>right</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_depth node_index  split_gain     threshold decision_type  \\\n",
       "0           1       0-S0  333.333008  1.000000e-35            <=   \n",
       "1           2       0-S1  166.667007  1.000000e-35            <=   \n",
       "2           3       0-L0         NaN           NaN          None   \n",
       "3           3       0-L2         NaN           NaN          None   \n",
       "4           2       0-L1         NaN           NaN          None   \n",
       "\n",
       "  missing_direction     value  count  \n",
       "0              left  0.000000   1000  \n",
       "1             right  0.066667    750  \n",
       "2              None  0.200000    250  \n",
       "3              None  0.000000    500  \n",
       "4              None -0.200000    250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(verbose=-1)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([0, 1, np.NaN, np.NaN], N / 4),\n",
    "    'y': np.repeat([1, 0, 0, 1], N / 4)\n",
    "\n",
    "})\n",
    "lgbm_params={'verbose': -1}\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMClassifier, lgbm_params)\n",
    "display(predict_and_evaluate_classifier(df, [\"X\"], \"y\", model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fc432b75-ba9f-4f16-9a69-80d4ec6f8507",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 0.75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>y_pred_proba</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.365834</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.634166</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X  y  y_pred  y_pred_proba  count\n",
       "0  0.0  0     0.0      0.365834    250\n",
       "1  1.0  1     1.0      0.634166    250\n",
       "2  NaN  0     0.0      0.500000    250\n",
       "3  NaN  1     0.0      0.500000    250"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_depth</th>\n",
       "      <th>node_index</th>\n",
       "      <th>split_gain</th>\n",
       "      <th>threshold</th>\n",
       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0-S0</td>\n",
       "      <td>333.333008</td>\n",
       "      <td>1.000000e-35</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0-S1</td>\n",
       "      <td>166.667007</td>\n",
       "      <td>1.000000e-35</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>right</td>\n",
       "      <td>-0.066667</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_depth node_index  split_gain     threshold decision_type  \\\n",
       "0           1       0-S0  333.333008  1.000000e-35            <=   \n",
       "1           2       0-S1  166.667007  1.000000e-35            <=   \n",
       "2           3       0-L0         NaN           NaN          None   \n",
       "3           3       0-L2         NaN           NaN          None   \n",
       "4           2       0-L1         NaN           NaN          None   \n",
       "\n",
       "  missing_direction     value  count  \n",
       "0              left  0.000000   1000  \n",
       "1             right -0.066667    750  \n",
       "2              None -0.200000    250  \n",
       "3              None  0.000000    500  \n",
       "4              None  0.200000    250  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(n_estimators=3, verbose=-1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([0,1 , np.NaN, np.NaN], N / 4),\n",
    "    'y': np.repeat([0, 1, 0, 1], N / 4)\n",
    "\n",
    "})\n",
    "\n",
    "lgbm_params = {'verbose': -1, 'n_estimators': 3}\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMClassifier, lgbm_params)\n",
    "display(predict_and_evaluate_classifier(df, [\"X\"], \"y\", model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27bac25d-869d-456a-9bdf-e4f2710cb6b1",
   "metadata": {},
   "source": [
    "## NaN not on training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "490427ce-de98-4850-9954-54b2bdb14590",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "   node_depth node_index  split_gain  threshold decision_type  \\\n",
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       "LGBMClassifier(n_estimators=3, verbose=-1)"
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SpNdLkkbT+1oKDpakn/1MXENO56W7jtSkHRUVlXOiogJefFHENN95p/frer3IDPjqK/jgAyESvJKKq1wubDYblZWV6PX6s5rFO51OtmzZgsFgYNCgQd1eGzhwIMOGDaOzs5NVq1bR0dFxSY754MGDvPbaazidTiZPnkxqaiqjR49m4cKFAKxfv55t27ZRVFR0xn1de+21jB8/noCAAOLi4nj88ceJiIigra2NgoIC1q1bh8vl4j//+Q9btmzBZDIxffp0oqOjWbhwIYMHD6axsZE1a9awe/duLBYLGzdu5L333kOSJKZPn05iYiLTpk1j9uzZOJ1O1qxZw44dO6g+XanAy4ROJzJp/v53+OwzIRxMSuq9XX09/PWvIgXxVJk5FwM1TKCionJWuFyii+Bzz4kCQmVl3QsH6XQQHw+LFsH3vy+6Bl4JxVTOB5fLRUNDA59++ilr1qyhqakJb29vEhMTWbhwIdOnT0en01FZWcmBAwf497//TV1dHXa7HT8/P1JTU7njjjsYNWoU2dnZrF69mtWrV1NdXc3q1as5ePAgJpOJkSNH8uqrr/b6fEmSKC8vp6WlhfDwcIKCgroZEIGBgZjNZlwuF3l5edjtdk6cOMGmTZv49NNPufXWW7nttttOG4I4E52dnZSXl3Po0CEAoqOj8fLywt/fn9jYWOU4Dx8+zIABA0hISOhzP1qtljfffJOwsDB8fHzQaDRoNBp8fX3R/e/H4XK5sNlsdHR0cPToUcrLywkMDCQmJgatVktMTAyBgYEAOBwOtmzZQmxsLAUFBRw7dgyNRkNMTAweHh4EBgYSGRmpHN+ePXtISkoiPDz8vM/FpUD+d+r1MGCACKdNnw7vvy/SEjs7u7Z1OoUmx2YT19dNN138JlyqMaCionJGOjuFJuC110TqYHV192yBgACRHTBlivAKJCdffl3AhVBcXMy2bdv49NNPSUpKYuDAgbS0tFBTU8Pbb79NamoqERERtLe3U19fj06nY8qUKbhcLnJzczlw4AB2u52RI0cSGBjIkCFDqK2t5cCBA0RHRzNz5kwCAwOJj4/v8/MlSaK+vh6Hw4HBYMDLy6vb60ajES8vL7RaLbW1tbhcLiwWizJ4T5o0CefpWj6eBc3NzdTX19PW1gaAr68ver0eDw8PTCYTWq0Wl8tFeXk5tbW1p9yPRqMhLS2t1/qmpiblGD08PIiIiKC2tlYRGep0OkWLYDKZMBqNaDQaJEnixIkTVFVV0djYiMViQa/X4+fnh1arxdPTE29vb2Xb4uJimpubL+hcXGq8vYWmICBABAe0WqEpKC/vus6am2HPHmFcO51w++3ifRfrOlONARUVldNis4kMgY0b4e23RStXd9F9UJAwBG69FWbO/HaUz5cSl8vFsWPH+OKLL8jNzeXnP/85Q4cOpbCwkC+//JK//OUv3Hnnnfj7+6PT6QgODmb27NncdNNNaDQalixZwuLFi/nss8/4y1/+QlRUFNHR0YSEhPDhhx8ydOhQfvKTnxB9mhMlSRItLS24XC5lAHZHp9Oh1+vR6/XKdl5eXkRGRjJ8+HAiIyOVWff50tTUREtLi5JhYTQa0el06HQ6PDw80Ol0igelpaXlrPcrSRKSJJGXl0dHRwdeXl6EhoaSkJBAXV0dtv+NfhqNRhH+yedA/syqqiqqq6tpb2/vdnxarVY5Pr1erwggLT3z+K5AtFoRTpsyRWTfSBJs2yZEhHIUqL5e9PZobhYljUeP7ipSdKGoxoCKisopkSRRSGjTJnjySdGgxR2jUZRU/e1vRapgjzGrX2K328nJyWHHjh3MnDmT6dOnYzAYCAkJQa/X8/LLL7Nq1SqGDBlCQkJCL/d4WloaQ4YMYe/evTQ2NhIaGnpetQvsdjuSJKHVavtM3dNoNOh0Omw2G5IkkZKSQkpKCg8++OB5f3d3Ojo66HTzVbtrHTQaDQaDAbvdjsViwWq1ntO+7XY7a9eupbm5meTkZKZNm0ZiYiKbN2/G9b/Yk0aj6WYEyYN8Z2cnra2ttLW1dTMc3LfVarXK8bW3tyvb9Qc0GpFx8PvfCy3Bv/4lsgtkA7y5WfT4+PGPRSZPaKgINVyoQaAKCFVUVE5JS4sQCf7yl6JwijtRUfDDHwpvQULCldmW9XwoKyujsrISSZIYMGCAMgDq9XpMJhOhoaHk5+efcgCUZ6cgRICSdH61C+R0QafT2afLX5IkHA7HJUsrPBPy95I1AGdLXV0dr776Kvv37ycxMZEHH3yQp5566pw/80zbne95v1IICYF77xUagpkzu/fosFpFZcP77oPc3DO3/z4bviOXr4qKysVETmx65RVRAKWnGHvMGFFL/d57uyqk9WeNgDudnZ3Y7XYaGhp4/fXX+fzzz5XBx2q1UlVVRUREBC6Xi5KSEg4cOMCqVasoLCzEYrHQ1NR02kJCZ4NGoyE4OFiZ+fc0POx2O1arFbvdTkhIyHmFBDo7O9mzZw+/+tWvFF2AzOzZs5kwYQKebn2iHQ6HMsBKkqTMtr29vbttdzoKCwvZuHEj//jHP5gxYwY//OEPGTFiBN7e3gD4+fkp30WSJOxu0nmHw6E89/Pzw8/PD4PB0Oe27sfn6+urbNef0GjEtZWUJLIJfvQjUahI9s65XKIp0vvvw8KF4pq8EFRjQEVFpReSJFyRGzeKoiju6UxDhsC8ecIY6O/6gL4wGAzo9Xo8PT3JyMhg9OjRvdz0kZGReHl5sX37dlasWEFbWxsjR47EYDBQVlZGdnY2Bw8ePO9j0Gg0mM1mvLy8sFqttPaIz7S2ttLS0qKo6M/HGJAkiaamJg4ePNhLYCe77v39/RUhntVqVbwUdrtd8VYEBQXhdxbS9hMnTrBhwwbWr1/PkCFDuPfeexkzZgzBwcFotVokSSI0NLTbAC8bQXa7HYfDgcvlQqvVEh4e3i07AYSh5nK5FKNBPj6z2XxBWRWXE7lYUXq6KNr1xhuiyqf872pthXXrIDwcEhNF06PzRTUGVFRUuuFyCTfk8uWQk9N14wFRWnXuXKETGDbssh3iJcXHxwdvb298fHyYNGkSTz75ZC8BH4iMg927d7N582ZuvfVWHnnkEYKDg9m3bx9Lly69YGNATh+0Wq3dSiXLJYyrqqrQ6XSkpaX1eXxn+xmZmZm0t7d3ey05OZmIiAhKS0vx8/OjubmZ1tZWHA4HNpuN1tZW5XiioqIwm83YbDZKSkpobm4mJCSEwMBA/Pz8lMyILVu2sG3bNpqamnjggQdYuHBhLyPGbDYTGBiIp6cnTqdTESa2tbVhtVqRJAmNRkNKSoqScunj40NHR4cipLRard2EhXFxcfj7+5/z+blS0GhEBsHNNwsxYWen6P/hcIjXDx2C7dth8GCYMaPrPeeKagyoqKh0w24XaYSvvy40AzI6nXBFPvywqCfwXSUoKEjJaT9+/DgOh6NPAWBZWRl1dXX4+vpyww03EB0djUajUWa5F4JGo0Gv1zNq1Ch27NjBiRMnsNvteHh44HK5yM/P59ixYxiNRiZPnozRaMTlcuFyuXA6nYrq/3SxdaPRyMSJE9m4ceMptykrK2Po0KFs2bKF0tJSOjo6aG5upri4WDnOjIwMkpKSqKmp4emnn+brr7/mrrvu4qabblLSLVeuXMnrr7/O4MGDee655xg1ahQOhwOHPKIhtBZGo5HBgwdTUlJCQ0MDpaWluFwuSktLafyfaMXDw4OJEycSFBREQkICAwYMICsri5KSEqVWQXl5ubLPUaNGKXUH+iuyh+CRR8RjWZm4RkEY71u2CE3BlCnnr91RBYQqKirdqKwUVdFaWrqKCul0orvgv/4lQgPfFX1AX3h4eChdAZcuXcrbb79NQUEBVqtVKcRTVVWlzHxbW1vZvHkzHR0dlJSUcPToUY4dO3ZRjuXBBx8kOTmZrKws/vKXv9De3s7Bgwf5+OOP2b9/P7fddhspKSno9Xqys7P5v//7P5KTk3nppZd66QDOh2HDhvHDH/4QnU7H1q1byc3NZffu3SxbtgyAWbNmMWHCBIKDg8nPz+fzzz+nqamJzz//nB07dmCz2cjNzeXxxx8nKyuLxYsXM23aNHx9fbstMTEx3HXXXTgcDu677z4mT55Me3s7mzZtorS0lC+++IKjR48SGBjI3LlzGT16NF5eXkyfPp3vfe97aDQaNm3aREFBAZs2bWL9+vXodDrmzp3LuHHjCAsLu+BzcSUQFCQKDv3mN93X19SIpkerVnUvBHYuqJ4BFRUVhc5O0SDl88+731QiIoRyOSrq4uU1X04kSWLJkiUsX76cPXv20NDQwCuvvEJeXh4//OEPGTVqFD4+Pnh5efHBBx+wePFijEYjgYGBpKam8oMf/IDIyEimT59OTU0NH330ERs2bCAtLY3Q0FDS0tL45ptveOihh3jsscfIz89n1apV1NXVKSl106ZNY9KkSWRmZp7yOOPj43nwwQfZvHkzGzZsYNmyZXh4eBAWFsYdd9zBrbfeqqj5JUnC6XRis9kuKIvBneDgYCZNmsRf//pX3n33Xf70pz8psfhbbrmFn/3sZ6SkpODt7U1sbCzTpk1j9+7dTJs2jaFDh+JyuWhvb1fSJCVJUlIH3bHZbIqXIC0tjfvuu4+oqCi++OILFi1aREdHB6Ghodx3333cd999SppjVFQUc+fOxWaz8Z///IcnnngCu92O0Wjk9ttv52c/+xlms/k70+JZoxEljCdMEMW9vv666zqtq4P//lfoec6nxIRqDKioqChUVcGxY+JRxtNT1BCYN0+om78j91USEhKYPn06GRkZSqe7xMREfH198fPzY+DAgdx1113k5OTQ3t6ORqPB29ubyMhIgoODMZlMZGZmYjAYOH78OJIkER0dTWhoKADh4eEEBgYSHR2Nh4cHWq2WCRMmoNVqCQoKIiUlheDg4NMeo6enJ4MHD8ZkMhEeHk5NTQ2enp5ER0eTmppKXFycsm1YWJgy6x49evRF6dTn4eFBSEgI11xzDT4+PtTX1+N0OjGZTCQnJzNw4EAltdFsNvPQQw8xa9YsMjIyGDhwIHq9nsjISJ599tnTVkQ0Go0kJCSg1Wrx9vZm4MCBmEwmYmJiqK2tVVoYJyUlkZqa2u19UVFRzJs3D19fX+rr69FoNPj7+5OYmEh6evp56SmuZIxGYZwvXNhVohhEYaL9+4WXQG6XfC5opP6ejKmionLR2LoVPvlElB2WiYqCOXNEiOBiFDdRUVG5MGw2oRuYMEEUBZPtLJ1OeAtGjBBi33NB1QyoqKgAIp2woEDMLtwZNEgIk74L4QEVle8CBgPExQlBr/ugL0nCW3AO1aEVVGNARUVFobxchAncGTQIJk68PMejoqLSNxoNXH+9EBXKSJIw5ntkip4VqjGgoqICiHoCDQ3d+w94eYmCJv08M0tF5TuHRiMKgLnXU5IkOHGie/vjs0U1BlRUVADRjbCjoyv+CKJ7mq9vV8lhFRWVK4fISBEycKe+vnvF0LNFNQZUVFQAYQj0bO4mGwKqVkBF5cojMLB3kaG2tvOrNaCmFqqoqACivGnPm4hef345y1cC7hX5+kKu8if3HZDz4OUcfTnRSqfTodVqz7o7n5xL757rL7chlj9L3o/8Oe5NgOQqhnKpXrmGQF/buR9bT+TPl7e7VLifN7mGgHxcfbVePtU+5N4D0NX58Wzff7XSl6jXbj+/LobqmVZRUQHAx6d3OKC1VfQp6I8cOnSIp59+ule1O7mOwMiRI9m7d2+39+zevZsHHniAYcOGERERQWpqKs888wwnT548bZ68OyUlJXz66afMnDmTsLAwEhMTufnmm3nzzTeVTnoyLS0tbN68mdmzZxMbG0tcXBwTJ07kd7/7HZ1ugd/W1lZ2797NnDlziIuLIy0tjRtuuIHFixef8jhef/11fvvb3/Lxxx+fw1k7dxwOB5s3b+bRRx8lOTmZsLAw/v73v5OXl3fW+ygpKeHRRx8lKiqKgIAAfvrTn3Ly5MlLeNTfDRobu3oUyAQEqEWHVFRULoCAACFG0mi6ZhY1NeKGY7WK4kP9CXm2aj9FANVutyszUbnJzk9+8hMGDBjAj370IwYMGEBpaSkvvfQSFouFhQsXMkPuBHMKysrKeO+99/jqq68YPXo0v//976msrGTt2rV8+OGHNDQ08PTTT6PRaKivr2f79u0899xzTJo0iaeeegqbzcahQ4dYsmQJkiTx4x//mICAAI4ePcqvfvUrYmJiePLJJ6mrq2P37t38+c9/ZsiQIWRkZCjd/pxOJwUFBWzYsIGbb76ZmTNnXtwT24Ovv/6adevWUVFRwYcffsiGDRsIDg7uZficjvDwcJ5++mlSUlJ49dVXu3lAVE7NyZO9jfXzKTgEqjGgoqLyPzw9hUEQGCiyCkBoCKqrRcphUtJlPbzzwmQyceONNzJ48OBervKQkBBiYmIAaG9vZ+3atZSVlXH33Xczc+ZMoqOjSUxM5Msvv2Tfvn3ExsaSmZmpNDHqi507d3Lw4EGcTifz589n9OjRNDY2UldXR2VlJStXruT+++8nODiY48ePs23bNmpra5k/fz4jR47E4XBgMpnYt28fK1as4MYbb6Sjo4OCggKys7O54447GDNmDO3t7XR0dLBs2TL27t1LWloaBoMBSZKwWCx89NFHDBkyhAEDBhASEnJJz/Hhw4cpKioiKCiIkSNH4uPjo1QuPFuMRiPx8fHEx8crRs25IIcZli1bxoABAxg8ePA576O/IUmiq6jF0rVOoxH1B85H8KsaAyoqKoCYTZjNouKgbAyAqHR2/Hj/NQYWLlzILbfccspBRpIkWlpaWLlyJWazmdGjR5OSkoJGoyEmJoZrrrmGP/zhD2RnZ1NSUtKnMSDPYrds2UJlZSWJiYlMnjwZELPekSNHUlxczN///nfy8/Px8fEhOzubvXv3EhUVxdSpU5XYf3p6OjNnzuTxxx8nLy+P8PBwKioqsFgsjBs3Dj8/P0JCQkhJScHf358jR44o3g+LxUJRURFbtmzh+eefJzEx8ZLqBQAKCwtpbm4mOTkZLy8vhg8ffkk/ry8kSaKyspIPPviAW2+99TtvDEiS0Pfs3y8EgzIaDaSlnZ8XT9UMqKhcZOSe752dncricrkUYZndblfWy6IpeWbj/r6eDWfcG9G479u9CcyFEhUFAwZ0X3f8OOzcKW4+30XPrcvlorW1lR07dpCRkYGvr68iyNNoNIwdOxZfX19KSkrIyso65X4cDgcHDhzA6XSSnp7eTXAYFxfHkCFDcDqdbN68mZaWFgoKCigqKmL48OHdtvX392fMmDGA0DCcOHGCtrY2NBoNvr6+iqjOw8MDf39/Ghsbld9QYWEhn3zyCRMmTGDw4MEEBASc07lwFwP29TtzF1e6XC5sNhv19fVKh0T33/uZPqOv66SnLsP9c9y3c782JEmis7OTL774grKyMtra2pRt+rrm5NfcBYv9DUkSXrt166CpqWu9RiNKEbvXHjhbVGNAReUi88wzzyiDSmBgIEOHDmXr1q00Nzeze/du7r33XkJDQ4mKiuIPf/gDhw4dorGxkd/97ncMHTqUqKgokpOTueaaa/j888+VWV9lZSXLly9n6tSphIeHExERQVpaGrfccgut7pWCLoDBg2H69O7rcnJEvfMTJy7KR1xxtLS0UFZWRnt7O5GRkb0a/MTHx+Pp6UltbS0nTnMSioqKaG5uxsvLS2lWJBMUFERERAQul4vs7GxOnjxJbW0tkiQRFRXVbVsvLy/i4+MByM/Px263ExAQgCRJysAPYuCtr68nLCwMnU5HcXExu3btYvfu3Tz11FP4nmtx+v8hSRJvvfUWCxcuJD09HbPZTExMDIsWLWLVqlVUV1fT0dHBhg0bGDNmDGvWrGHfvn389a9/xc/Pj/DwcLZv337az3C5XLz22mvMmTOHpKQk5TOefvppysrKuh3Lzp07lWOJjIwkNDSUMWPG8MEHH1BcXExBQQG/+MUveOaZZzhw4ACPPPIIoaGhjB8/HkmSOHDgAD/96U/JyMggKiqKkJAQRowYwf/93/9x6NCh8zpHl5vmZliyRHQYlaUZWq0QAc+eLcJ954oaJlBRucg89thj6PV6VqxYQXV1Nf/85z8ZNmwYJpOJIUOG8OSTT9LY2Mj06dOZM2cOVquVf/zjH7z77rs8/PDDDBw4kLq6OrZu3cozzzxDQkICiYmJbNy4kTfeeIMpU6bwpz/9Cbvdzt69e5UB42IQHi4MgqQkIU4C4REoKRHNi/72t/5Vc6C5uZlly5bx9ttvY7fbMRgMBAUFkZCQwF133UVycjJWq5XGxkZAhBV6utWNRiNGoxGbzUZzc3OfnyNJEvX19djtdjw8PPDy8ur2ul6vx9PTE4PBQH19PXV1dXR0dChd+tzRarXKZzY1NREQEEB4eDgBAQEsWbKEhx9+mNraWnbs2EFDQwPTp09Hp9OxbNkyCgsL+elPf3reHQvb29t5//33efXVV5k6dSqLFi0iNjaW+vp6XnvtNV588UWuueYaHn30UUaPHs3777/PT3/6U8rKyhg1ahQ/+tGP0Gq1pKSknPIz6uvr+fjjj3n55ZeZMWMGd955J6mpqej1er766iveeuutXud12LBhLFiwgLS0NDo7O/n1r3/Nq6++ikajYdGiRfz4xz/m+PHjnDx5krvuuosFCxZgNBrRaDQ0NjYSExPDPffcw4QJE3C5XLz44ousWLGChoaGyxLWuBDsdqHh+de/uhcX8vODa64Rj+eTkakaAyoqF5mYmBjGjh1LUVER69atw2Kx4OnpiV6vx8vLC7PZjMViYerUqcTExLB161bWr19PYGAgM2bMICkpiebmZrRaLR988AE7duxAr9dTWlpKcXExP/jBDxgxYgSSJGE2mykpKek1+JwvBoOoajZnDrz+eldYoLERNm6Egwdh4MD+kVkQHBxMZmYmLpcLHx8fJElSBvSsrCxaW1u577778Pf3V9L49Hp9t5x92X2v1+txuVxYT5Nn2dHRgSRJ3WoEuO9Hq9Wi1+vp6OjAarXicDiUfffcVqPR4OHhQWdnJ3q9nuTkZG655RaOHDnCq6++SkdHB42NjcyaNYthw4axe/duGhoaiIiIYPDgwWzYsIFjx47R0dGB0WgkOjqaWbNmdQsz9MTlctHS0sJnn32G0Whk9OjRTJ06FbPZTGtrK/n5+Xz44Yfs27ePQ4cOMW3aNPz9/ZVQhayNOB1Op5P6+no+//xz9Ho9U6ZMYeLEiURERKDRaMjPz+9myGg0GqXVc3h4OJGRkdhsNgYOHMiaNWuoqKhQWkf7+/vj6elJfHw8mZmZyj7i4+Px8/PDYDCQlpYGwODBg/nqq68oLCzEYrHg5eV1VjUkrgQKCoSn7tixrutToxE9CubPP//OoqoxoKJyCRg0aBBDhgzhq6++4uuvv2bKlCkYDAY6Ojqorq7GarWSmJioDPI5OTksWLBA6ePu7+/P2LFjMRqN7N27l/T0dCXO2dLSQnV1NcHBwaSnp19UsZR8U7n+evjwQ9H9zOUS6Uu5ubBypRAZhoefX/rSt4nZbGbixImKe9jDw4Pm5maOHTtGRUUFH3zwAWlpaQwdOlRxvZ9qQJDXn00s/HSDikajUYohydv2tb283uVyodFoiI2N5a677uLVV18lKysLo9FIUlISs2bNIjAwkI0bNxIYGMigQYOwWq189NFHlJWVYbPZ0Gg0BAUFYTabGTdu3CmFlHa7nYaGBrZv385NN93E0KFDiYuLA0To4sYbb2Tt2rWUlJSwd+9epk2bdsrveSrsdjt1dXXs2rWLOXPmMHr0aBISEgBxbr28vLoZK1qtlgE9RCx6vZ60tDTWrFmDxWLBarWe1hhOTk7uc93WrVuxWCy0t7dfNGP6UtPUBPv2ievQvTOhry8kJsLUqefnFQDVGFBRuSQkJiYyZMgQQkNDWbFiBU8//TQ+Pj6Ul5ezc+dO5s6di6enJ9XV1dTU1OB0OomLi1OETiCquAUFBVFaWorJZCLyf92Cfvvb31JeXs61115LWloaISEh3arVXSh+fuKmMmqUEA7KamW7Hf70J9EcZcIEkc98JU+mvL298fb2JiIiQlkXGhqqzGa/+uorNm3ahFarVdLgeua3y3/Ls/jTpb0ZjUa0Wq0y2LvjLpqTQwA6nU5Z19e2clhDp9Ph4+PDsGHDeOedd7pt53A42LlzJ0eOHOHhhx8mJSWFpUuXsmzZMpYsWUJycjL79+/nL3/5C8899xxLly4lKCioz99Ja2srRUVFdHR0kJKS0k1zIGc5BAQEUF5eTn5+/tn8C3rR0tJCRUUFnZ2dShri6ZAFgLLx1PN8ya+fDvn98j7k953t+68EJEks27fD8uXCS+fOkCGwYIEw0s8XVUCoonKJSEpKYtGiRRQWFir55IWFhaxfv567774bo9GoKJubm5t54YUXCA0NVarkDRo0iJKSEtrb29Hr9UrFubFjx/KPf/yD2bNnM3PmTJ5++unTuq/PB4MBXnlFzDbcvdgWCzz+OLz3nihI1B8xGAwMGjQIDw8PKioqaG9vJzg4GBADYk9Fu+zWNxqNp8yd12g0hIaGotfr6ezspL1HD1m73U5HRwednZ2Eh4cTFhaGyWTC4XAoSnwZl8ulfGZwcHAvTYH7dvX19Tz++OM8+eSTTJgwgZqaGjZt2sSMGTMYNmwYcXFxDBs2jJtvvpnNmzfT1NTUy/iQsdlsihC1L+2EvF6v1/f6fmdLW1sbtbW1gAjjeJzBveRyuVi/fj333HMPI0eOJDY2lrCwMJ599lmqqqrO6jO3bt3KU089xeTJk4mPjyc4OJjHH3+8V/XJK50tW+D554Ux4E5MjAgP/OAHF7Z/1TOgonIJ0Gg0REdHM3fuXF588UVWr16Nr68vTU1NmEwmoqKi0Gq1mEwmvL298fX15YEHHuCmm27qNfs0mUzExMTg6enJmDFjePnllyktLWXdunXs2LGDlStXkpqayr333nvGm6vDAQ880BUOCA+H6GhISIDYWKFClj2mCQnw8MPwwQdiRiJTXQ3vvgsVFfCLX1z5HoK+kGeEOp1OUcB7e3tTXV3dq3JeWVkZnZ2dBAUFERsbe8p9RkZG4ufnh9Vqpb6+vttrzc3NVFdXo9FoSE5OJiYmhsDAQLRaba9BzWq1Kor6+Ph4/P39+/y8wsJCli5dyty5c0lPT8fX15fOzk4aGhqIi4tDp9Oh0WgwGo0EBwcr2Qjh4eF9/k48PDww/S8nraOjo8/yyx0dHWg0GsV4Old0Op2ikXCvANkXdrud8vJyfvaznxEbG8ttt93GiBEjMJlMLF26lPfff/+0n+VyuaipqeF3v/sdDoeD0aNH8+tf/5rAwEC+/PJLVqxYcV7f4dumowOOHoWf/Uyk+br/PI1G+OEPYd68C+8sel7GgNUqbgT/E+CqqHxn0Olg6NCLM7h5e3sTGxtLRkYG+/btIywsjPDwcIYPH66IpGQvgF6vx8PDg2HDhvU5E7RYLLS2tuJyuUhOTiYqKgpfX1+cTicrV67k6NGjZ11noLwcCgvFd/X3FxUHQ0IgOFg89/MT68xm4SFISBDvKSoS77fbhYhp/XphPDz4oHh/z+5pl5u8vDyOHj3KjTfe2G29w+GgvLwcl8tFaGgoZrMZPz8/0tLSKCgowGKxKPF8SZLIycnBYrGQmJhIenr6KT/Py8uLxMRETp48SUlJSbeGQlVVVRQWFqLT6Rg+fDj+/v5ERkZiNps5ceJEN71Be3s7OTk5AAwcOLBPb0RdXR25ublkZ2fz8MMPExgYiF6vR6fTKZkPMi6XSwk9yeGJUx1/WFgYBoOB8vJyOjo6lNckSaKiooK2tja8vb17pUOeLSaTSfk+RUVF3fov9KSzs5Ompiby8vKYPXs248ePJzMzE4PBwO7du89o+EqSRGlpKfn5+YwYMYLx48czbtw4fHx8OHLkiGL4XMnU14vU3v/8B7Kzu5ce1mhEaGDKFGHIX2hPp/O6fNvaYNcucXAqKt8ljEZhDFwM5FnntGnTePPNN9m8eTNTp05l3rx5yjbyzTEwMJDs7GyqqqqIiIjAYDCg0WgUl25zczOVlZU0NzczduxYPD09GTx4MIcPH+abb75RlOxnQ2goZGV1rzIoYzAIYyAqCuLjhdfAbhfGgWwMgJit5ObCW29BaiqMGyf2eyXpsPLy8liyZAlTpkzBz88PnU6Hw+FQRHJarZa0tDRiY2Px8fFh8uTJfPbZZ5SUlBAXF4evry8dHR1s27YNrVZLQkICqampysBYV1eHr68voaGh+Pj4oNFoGDFiBNXV1RQUFFBWVkZkZCRWq5X8/HxycnIIDQ0lIyMDb29vkpOTSU1NZdeuXZSWlhIREaFU0tu1axchISEMGjSolzHgdDrJzc3l+PHj+Pn5MW7cOEUD4OXlRXh4ONXV1crA3dzcTGlpKf7+/vj5+Z1yEJXfGxsbS35+PhUVFcTGxuLt7Y3NZmP37t00NTUREhJyWqPodPj4+BAREUFQUBCHDh2ivLycoKAgPD096ezspKamRvnNO51OmpqacDqdxMbGEhkZibe3Nx0dHbS3t/fyKuj1eqWQkVxwqLGxEYfDgdlsVrIK7HY7FovllOGSy418GdfVwaFDsHat8M65ewQ8PMQ1evfdkJ4uBIQXynkZA/X1sHQpLFv23axIpnL14u8Pzz578dzenp6e3HzzzbzzzjscP36czMxMRo8erbzu4eGhlK795JNP+PTTT5k7d67i5q2vr1dy07ds2cK2bdsIDg4mLi6O0tJSZSY7dOjQs2r3qtGIGgKnSg202cRNSL4RnQ67XZQq/v734Zln4LrrRPVCrVZ8Ts/l28ZqtVJeXs6aNWuYNm0aPj4+NDQ0kJWVxR//+EfMZjOzZs1i6NCh6PV6fvCDH/DBBx/wxRdf4HK5GDNmDLm5uXz88ceMGTOGcePGERERgdPp5O233+bDDz9k4sSJPPDAA4wdOxaABQsWUFRUxOrVq3nrrbf48Y9/TEFBAV999RX79+/n+uuvV/63Y8aMoaWlhdWrV/Pmm2/y/e9/n87OTrZu3coXX3zBggULSE1N7VU8qL29nTVr1lBVVcWzzz7bLSPBbDYzduxY/vjHP3LkyBGSk5PJzs5mw4YNTJw4EV9f31N6BnQ6HQEBAdx999387W9/Y+3ateh0OgYOHEhFRQV//etfaWpqYvr06cyePVsROcrCPLlq4enaKnt6ehIZGcnMmTNZtmwZK1aswG63k5ycTHl5OStXrlQKK2k0GsLCwtDr9Rw5coT09HQCAwOprKwkJydHqVgoD+qBgYGKQLG8vBxfX19CQkLw8PCguLhY2UdlZSXHjh2jsbGRgIAApUbHmTJBvg3k8dRqFdqATz8VHjj3cVajgbAw+MlPRArwRfPISefB8eOStHChJGk0ssZRXdTlu7H4+0uS03k+V0XfuFwuyel0StOmTZPuuece6eOPP5ZcLle3bex2u1ReXi49+eSTUmpqqhQQECAFBARIiYmJ0i233CJlZWVJJ06ckN544w1p5MiRktlslry8vKTw8HBp1qxZ0l/+8heps7Oz1377wumUpA8/lKTExIt73nQ6SfLxEftduFCSfvlLSfrvfyXp8GFJamu7eOfzXNixY4f00EMPSampqVJgYKBkMpmkoKAgacCAAdL9998v5eXlSRaLRXK5XMr/adOmTdJtt90mJScnS/7+/lJsbKz0+OOPSzk5OVJnZ6ckSZLkcDik5557TkpJSZHuu+8+aefOncpnulwu6cSJE9J//vMfafTo0ZKvr68UEREhXXvttdLLL78stbW1Kf8nl8sl1dfXS6tXr5bGjh0rBQcHS2azWRoxYoT09NNPS62trZKzjx/js88+Kz3//PPSypUrlWOXcTgcUl1dnfTEE09IaWlpUnh4uJSWlibdfvvtUlFRkeRwOE57zlwul2Sz2aSXX35ZmjVrlhQdHS2ZTCbJbDZL1113nfT5559L5eXlks1mkwoLC6UpU6ZIfn5+klarlXx9faUxY8ZIH3/8sVRTU3PKz3A4HFJ9fb300EMPSWlpaVJwcLAUGRkpTZ06VfrlL38pRURESAEBAdKsWbOk/fv3Sz//+c+l1NRUKSQkREpOTpbuuOMO6ZVXXpEiIyMls9ksLVq0SCouLpb27t0rjR8/XjKbzVJUVJQ0d+5cqaKiQvr9738vTZw4UQoJCZEiIiKkW2+9VfrTn/4kjR8/XjKZTNLIkSOlkpISyW63n9Pv61Jgs0lSYaEkzZ4tSUFBfY+xU6ZI0r//LUkOhySdxSV/1mgkSZLO1YDIzYWnn1Y9AyrfPfz9hfv8QuNv7kiSxK233so111zDlClTSExM7PW6w+GgurqaxsZGZaai1+sxmUyEh4ej1+tpaWmhtrZWqf2u0+nw9vbG399fKX97ppmNyyXylO+7T8QiLyYajXBfmkwiC+FHP4LJk0UXtUvcK6dP2traqK+vp6WlRenfoNVq8fDwwNfXVxHSyR4VSZJob2+npqaG9vZ27Ha7kt5pNpuVinaSJFz59fX1yuzTPUWus7OTtrY2ampqlCqDcu2IkJCQbjN5h8OBxWKhvLxcyQgxGo34+/srhXh6/k/Ly8vRaDTKPt2RJDFDr6ysVFzsOp0Ok8lEdHQ0Hh4ep/2NyMNBbW0tTU1NdHR04HA40Gq1+Pj4YDablYwCm81GYWGhIjbUarV4enoSHh6Or6/vKcMRkiTEm2VlZbS0tChpm56envj6+lJXV4fL5cLb25uYmBgaGhpoampS/h8+Pj74+PhQWVmJJEn4+PgQExOD0+mkpKREOY/e3t4kJCRQV1dHc3OzIn709fXFZDLR1NSkFARLTk4+47m51JSWwo4d8Pe/w5EjInPHXcOp08E994gaIJMmCY3PxeSiGQNGo4hh+Pv3P2WxytWJ09nbFX4xjAH5xqjRaHA6ndTW1vLkk09yzz33MH78+G9duORyiRh/a6sI8R05IkIhcrnhS0FgoBAWLlwoNBj9oWKhisq3jSQJIf6ePbB3rzAGvvmmu1BQqxWagIUL4ZZbxPX0v5IjF5WLpv/19YUxY0SpUtUYUOkPdHaeOS5+PpSVleHn54eXlxdWq5Vdu3YRHh6uzKouBZIkYvidnSLu39kplo4OMcNobBR1AUpKhBHgXr3sUtDUJETGcr+e+Piuv9X7g8rVjny91tQIMe+SJeJ6KSjovp3RKFKAhw2DRx4R4+sZ6jSdNxfNGAgJgZtvhhtuuLguVhWVS0VrK/zudxc/1PWf//yHzMxMkpKSaGpq4g9/+AOvvvpqr/DAuSIf56ki95WVQvFfWioG/YICkZeclye6nF2kXkbd0GpFaMDh6N7iWJJg82Yx4xk0SKie779fZCvI9wfVKFC5mpCvDZdLGOxVVfDOOyIs0NbW+z6k1YoQ2+zZ8Nxzwii4lNfMFZYZrKLS//H09OTXv/41xcXFxMXF8dxzzzF06NDz7iQnY7OJGfeJEyJUV1golpMnxYDf2tplGEB3Q+Fio9GIOgQzZsBTT8HixbBmTW8dQkeHmPkcPiy2eeIJmDhRVE1TUbnasNlgwwZYsUL0F6iqEsZBTwwGUVHwppuE7ubbqOGhGgMqKheZe+65h+uvv57Ozk6MRiNRUVFK3YDTIUnQ3i4KelVViZl+aamY7ZeViXh/S4tw/1utXaEA+fm3VWI9I0OkNE2ZIlIJIyJEmtOUKbBpk0iHcmtJjySJm2B2ttAqJCVBZibMnStudHIqoorKdxGHQxjxa9fCJ58Ij11NjVjX85o1GsX19fjjMHKk0AacbxfCc0U1BlRULjIRERHdmuO443SKwbu+HmprRSy/uVncGOrrxSKva24WYsa6OrGuvb174ZFzwcMDvL2FAtlsFsdRUSGWs31/SIjwBEycCMOHQ0qKEAqCKE5kNIptoqNFi9WdO7u6HoLwEhQWiu9YXg7FxXDgAIwYAWlp4rguR9aBisrFRjaA8/OFt+zQISEQ3LtXePB6Vnr29ha6mhEjYNYsmD5dXKun6Yt10VGNARWVi4zLJeLzNptYOjq6ZvIWixj4i4vFUl4uav1XVIg4f2Pj+bv1dToxaBsMYmA2GkVFQC8vUVUwKEi45xMShGGxceOZjQG9XmRYREYKEdPDD4vHvnrnmM3iBjZsmNheoxE3w5oaYdjItLSI5fhx0Xxl/nzhVUhPF8doNotjVr0FKv0Nh0Nc4/X1wju2dav4je/YIYyAnmi14veekgLjx8O114pr4XJw1RkDkiQpubh9NcnQaDRotVp0Oh0ulwun04l79qX82tlUe7vQ43T/fL1ef8qqXj1xOBzd2nX2pGcr1p6fJW8jf1f5ubytnBfvvn+dTndR2+heybif1r6EfJ2dws1fXi4G+Lw8MSjK7v7KyguL4/dV3U+jEQN+dLRI8Y2JEe74tDThyg8L616ytKhIDNKbN5/6c3Q6kQEwd64QBl977Zld+lqtGMhvu014EVauFCnI69YJA8n9kpNFj2+8IbogJiTAtGmiBkJamjBm3KsZqqhcabiLAl0u4cXLzoaPP4bPPxdGbx/9ntBoxPVlMsH3vicEtqmpF95s6EK46owBgNzcXJ5//nm++eabbt3F5EYjM2bM4KWXXmLJkiW88cYbHDlyhPb2doxGIwsWLOCZZ54579rcZ0t9fT2ff/45S5cuZdeuXfzzn/9k/vz5+Pn5nfG9v/zlL/nyyy/77Dnu7e1Namoqu3fvVgbt6upqduzYwZ///GeOHz+OwWBQzsNTTz3VrXFOZ2cnmzdv5qWXXuL48eNYrVZCQkKYO3cuzz77LAEBAUpXsu8qclpQebkY5E+cEINrcbEQ9hUUCI+ArK7vKeg7X+SBNjZW5BonJIhBPzYWkpOF8vhsSwFHR4sZiYdH7ywDOa/5scfETUoOAZzrgBwSIoqk3HCD8AL885/COJCFju5YLMKdeuwY/OtfIm46e7YwDiZO7NsToaJyuZEk4fX68kshCszKEjof2TjoC09PkSJ4zTUiwyYqShgGl9vg/W7ftU9BfHw8v/vd79izZw/Lly/nk08+AWD8+PHccMMN3HTTTQDMnj0bu93OqlWryMvL48UXXyQpKYnIC6z4YLfbqa6uZuPGjbS2tnLLLbdgNpsVj8WKFSv473//S05OjtJS9XQz/Z44nU7sdrtSyc4dm83WrUFHcXExq1ev5uWXX6alpYXXXnuNw4cPs2vXLhYvXkxLSwu/+93v8PX1paGhgQMHDvCTn/yE5uZmHnvsMUwmExs3buSjjz6is7OTxx57jIEDB17Q+blSaG0Vlr7s0i8uFjN+WdzX1tZdyGezdT2/EFe/2Sxm8hERYrCPjxdu9/Bw8ZqnpzAKjEYREjAYxDrZ2XM2NxWdTrjkIyKE90J+X2qqGHxvu014FEJChMFwPo4wefbj7y+MlxdegNtvF0VVtm0TlRDde8XIxpLLJYyC8nJRnz06ussokEMJqrZA5XLR0SGM26NHxeC/fbu4TzQ1ifBbX/2PZM/dwoVCE5CeLq5xs/nbEwieiavOGNBoNHh5eZGUlISnpycajYYtW7ZQVVVFZWUldXV1yrYdHR2cPHmSgIAAbr/9dqX95fm4wSVJorOzkyNHjigdwVwuF4MGDVJSztra2sjJyWHPnj10dnbS0dFBa2vreaWkZWRkMGbMGFJSUrqt1+v13bqg7d+/n2+++YaioiIyMjIYO3YsQUFB1NXVsXXrVtauXcsPfvADkpOTKS0tZc2aNeTl5ZGRkcHw4cMJDw/HYrGwfPly1q9fz4wZMwgPDycoKOicj/lKoLNTuK0bGoQx0NLSJeyrr+8S9jU39+3+Oxv0+q62wYGBYrAMCOhqIxwQIG4c/v5i4JO38/MTBUcuxo1DoxH7Dw8XxkBCAkyYIFT+w4bBqFHChXkxPkt2hyYkdH3vQYNEp8Ndu8RNtWfp1Y4OsdTWiplWQ4NIT4yLEwZSXJyIs8bHn7+xoqJyNthsQstTWtoV8isuFs+Li0Vq76m8ACaTMOQHDIDRo2HsWOEV+LbFgWfDVWcMuBMREcHkyZOZOHEiy5cvJz8/n+3btzN48GCuv/56tmzZwqFDhxg0aBA333xzr+5hZ4PT6aSzs5O6ujrKy8vZt28feXl5WK1WJk+ezKRJk5SqdDabjfLycnQ6HZmZmTQ2NlJeXn5e323IkCGMHj2aa6655rTHtn//fvbt24dWq2XgwIEEBgYyaNAgDh06hNPpVHrCh4SEUFJSwvr163G5XKSlpREWFkZcXByZmZkAnDx5kuzsbNLT0/utMWCzwSuviAv9QtL1ZDGf0Shm7d7eXaI+k6lrQIuK6vICxMV9u6lEwcEiNt/aKmbed9whblqX8l8XECAqlQ4bJjQFn30m1pWViQG/uVl4XGRcLvE8K0ss3t7inGVkCMHVyJHCuPDx6Vo8PK6MmZZK/0TO+GltFb/HujphMB86JLwA+/cLQ/VU9wbZGxYcLK7zIUOEKHDOnCtbGHtVGwNarZaQkBCee+45tm7dSm1tLXv37qW9vZ2kpCR+85vf8IMf/IBrr732nKrHye58p9NJe3s7hYWFfPLJJ3z99ddcf/313HnnnQwfPrzXjD84OJgbb7yRG2+8kXXr1nH06NGL+n17Ins+CgsL8fb2Jjo6Gp1Oh9lsxmw2K9vt3LmTlJQUKioqlGOKjIxUGotER0cr2x49epSMjAwGDx58SY/9UuFyibj/2XIqMV9AgBjk4+NFPD8jQ/wdEyMGsyuhVn9CAtx4o2hBLOf7f1sYjeJc/OQnIkNh716hul63TngLnM7uFQ1lLBYxM8vPF4aEj49Ix8rMFN6M8ePFeZdjsBqNWvFQ5dS4CwDlEFVrq9AAbd0qUmQPHhShwTN5ArVaYcj7+YlQwB13iN+k2dw/PFdXtTEAop98eno6Tz31FP/85z/Jy8sjKyuLqVOn8pOf/IRFixaRkJBwzvutrKzkww8/ZM+ePXR0dHDzzTfz7LPPYjQazzor4FJTVFREc3MzkiT6eMshEIPBgNFoRKfT4XQ6OXHiBPn5+VRXVysZGHLnMp1Oh5eXl7JtWVkZVVVVl/mbXXpkN3tKihjs4+PFkpIi3IC+vl0z/DOJ+S4XAwaI2CVc3uMyGkWIYvx4+H//T7hdlyyBr74S2oHT9VFoaxNGxNatXQN/XJyog5CRIYyECRPEDVpFpSc2m/BI7dghZv1ZWUIQXFPTZRycjf4nIQFmzhT1AaZM6TIArqTr/Uxc9caAPCjfeuutHD16FIvFQllZGVarlcTEREwm0zmlEdrtdqqqqrjzzjuJiopi5syZTJkyhZCQELy8vPpsSXop2L59O2vXruXFF1/EarUSGBhIaGgoAwYMYPr06YwaNYrW1tZuIkO5hadGo0Gn02EwGBTdQktLCx0dHd22lY0ajUaD0WjEYrHQ3t6utBDt73h6duXYy7N6WcgXHS3c6Z6eXe5/99z+K0EdfCauFOPE/Ti0WpES+dBDokNbaanQFGzbJm7YTU29sx/cszScTuHSrasTN3dvb+GlGTJEhEQSErqMtoCAK+P7q3w7WK1Cg5KTI7x/+fmiCFZZmfAGtLUJAWBnZ98iQHeCgsRvafhwYWwOHSrW+fl1TQT6G/3wkC8NYWFhJCYmEhYWRllZGS6Xi02bNjFs2DACAwPPWsQn9y6fOnUq9fX1lJWVsXv3boYOHYq/v7+Si38pycjIUDrm+fv709LSQlZWFllZWeTk5HDy5EkCAwOx2WzdMhR6Ginyc6fT2asuw6m2dblcfdZv6C94eAj3XkiIMAR8fbvEfUFB4m9/fyGEu5Ljf/0VjUYYWLLRJWdTpKWJUEZJibh5FxUJMVdfDV7kYk9NTeK5VivcvEeOiP9jSIgIJYSGdj0PDRW6jYgIcSPvD25dlb5xOERMv75e/N8rKkRhr9paMeOvrBTrq6uF0eheEOtUeHiI616u3ZGUJDxQcXHCMxgR0f8zXFRj4H80NDQgSRL+/v6EhYVRXV3Nl19+ycSJEwkJCSEuLu6s9qPT6QgMDOSxxx5jzZo1HD58mO3bt2O1WnG5XJjNZvz9/c/Z43AujB49msGDB2MymYiLi6O5uZl33nmHjz/+mH379pGbm8vEiROJiYnpNqi7GwZyISIQGQhy0aO+tgWUbd2LD/VHjEb44Q/FAOTnd3mLgFztaLXif+DnJ27AktRVy2H/fjHzLykRszqLRczqLJbe+3G5hAHh3i8BhHEn39ATE0XIJD1dGICyx8c9jdPDo3/O+L5ryLeevtp1W63id9DQ0FXw6/hxEXqqrOy7CmBf6HRCj+LrKx4DA4VhOno0TJokDIDAwO/WZOCq/2nLFfXWrFnDpk2b8PT05M477+Tll1+mubmZxYsXo9Vq+cEPfnDWg5xGo8FsNnP33XcrAsIPP/yQN998k+uuu46ZM2cydOhQJdYuv+di0bMgkqenJ48++ijZ2dkcOXKEzs5OvvjiC/7f//t/3SoR2u125Xy4XC5s/yuE7+fnh7+/f7fiQ3a7Xal9IKdNgtASeHl5XbTv8m2j0wlBmjozvLKQQwkJCWKZM0cMCvv3C/HhoUNiOXy4q9KhHPM9FQ0NYjlwoGudwdAVEpKLOQ0cKIyFsLCu0EJfOpCejyrnz6k6b8p/2+1drbp7tuuuqBBeoXOp9SHrTTQaYfT5+oqslwkThAGQni5m/99lrnpjoLOzk/379/OrX/2KX/ziF8yZMwedTkdOTg7r169n165d+Pr6EhYWxoIFC855/97e3gwcOJDnn3+en/zkJ3z22We8+uqr1NbWct111/H9738fX1/fSz6b9vf3x9fXF6PRiNPppLa2ltjYWPz9/dFqtUiSRGtrqzKwy54MgOTkZFJSUmhtbVWEgm1tbTgcDhwOBxaLRdk2JiaGsLCwS/pdVFRA3LgzM0XcVpKEXqClRWgLsrK6jIOiorPfp83WlUvec8A3GoUxkJQkxJfx8WKACAsTYY24OJE22o8dY1cMFovI7a+oEN6gigox+JeWioE/N1d4A9yrfLobDOeCwSBi/hMmiN/S4MHCE2UwXF3lsK9qY8BisZCfn8/Pf/5zHn74YaZNm0ZUVBROp5Nf//rXHDp0iNraWvbt28cbb7zBmDFjCA0NPSf3viyw02g0BAUFcdNNNzFt2jTKy8vJyspi8eLF3H777d1S+c6EPGA///zzZGVlMWrUKGbOnMmkSZM4duwY7777LvPnz2fy5MnKe5qbm2lra8Nms2EwGIiOjsZkMpGcnExiYiJlZWWUlpbidDppbGykpqZGee+4ceOIjIyksrKSIUOGcPDgQcrLyxVxYWlpqbLt4MGDexU6UlG52PQ1C9fphOt2yhSR0mWxCE1BeblIDzt5Uswii4rEcrpUsb5KRzscwg3d2Cjqz8vVH/X6rr9lPYncHTIyUjy6F5IKDhaPBsPVZTh0dor/R0uLiNM3Noq4fkNDVxfPykoR129rE25/94Zf8iK37D7XQV+rFYLS1NQuMWlCghCThoaKcIC3twgLeXpePUaAzFVrDNjtdnJycli5ciUhISHMnj1b6TvvcrkYPHgw8+fPZ8WKFVRXV3PkyBGWLFnCww8/rKjuzwWNRoNeryc0NJSgoCBCQ0Px9PTEarXieY5J5w6Hg/r6elauXElRURFNTU34+PgwceJEbDYbW7duJT4+ntTUVIKDg+no6GDNmjUU/W+K5O3tzfTp0/Hy8mLEiBHk5+dTUlJCTk4ODQ0N5ObmkpeXh06nIzk5mcGDB+Pv709sbCyzZ8/m8OHD5ObmUl1dDUBWVhYgPAiDBg264HLNKirng0YjBuaAALGAmDkmJIhZe02NGHTq6rqEZT3XnU5MJknCIHA4RFy6L+SiUiZTl/jUz6+re6SXlxh05L/lglTe3uI9sj7BvdS03IlSLmLl4dF9kW8fl2Lgkj0u8iBss4nv33OQlp/b7WIQt1i6YviyUWa1dl8nr29vF4+trcK939raNes/XwwGYZS5l/EODu4SioaHC+Go2SzWnU/vje8aV50xIEkSlZWVVFRUsHbtWr744gumTJmCv7+/0mDH5XLR1tbGgAED2LBhAy6Xi7q6Ot577z1GjhxJTEwMgYGB+Pj49Nq/0+nEYrFw/PjxM6rqPTw8CA8PV+L2drudhoYGKisryc/Pp+l/cmiXy0VhYSFHjhwhMjISk8lER0cHlZWVWK1WamtrqampUUott7S0cOjQIcxmM1FRUTQ3N/PZZ59RW1tLdHQ0KSkpzJgxA6PRyLBhw6isrOTAgQOUl5ezY8cODh8+TEFBATExMVxzzTXExcUpRYnmzp3LypUrqampISsrC5PJxO7duwkLC2P27NkMGDCAQLnJvYrKZUarFbNwf/+udS5XV6/5goIub0F+vjAQOjq6z0A7OrrSzc40G5XFbA0NZz422XCQy1EHBgoDQjYm5FmqbDS4GxSywFE2LmTcY9/u5+BUzkx5sHenp+tdVufLA7rcf6PnoC+fK3nm39raVcWvvv7CB/ieuHtkPD27iz3lDp7p6cITEB8vnoeH97/8/28LjXS23W/cyM2Fp58WrUnld6enw+9/LzqUXcniK0mSlI6EeXl5OBwOAgICePjhh7nrrrtITEykqamJxYsX88orr1BTU6MI6WRX//333891113H6NGje+2/vb2dQ4cOMWfOHOV9p0Kn03Hbbbfx3HPPERsbS3V1NStWrOCll16itraWzs5OnE6n4lWQPRjXX389M2bM4IEHHmDLli1MnTqVm2++meuvv56amhoef/xxcnJyaGxsVEIDQUFBDBgwgMmTJ7No0SLS0tIU70ZVVRXbt2/npZdeIjc3F6PRSFJSEjNmzOAXv/gF3t7eyrYdHR1s3ryZP/3pTxw/fhybzYbZbGbu3Ln88pe/7FddC1tbxY3Y/Qrw9xc38iv5N6xyaZAk8b+XDYSSElGA5tgxsa6xUQx2p+pEeSEdKS8WOp0YEOVLUKMRg2VfXR/lY29p6f4d5MH+SqCnQNN9CQkRA3xsbFc2SEqKeG42i/OgcvZclcaAnAvv/tW1Wi06nQ6NRqNs4zxFUFGr1SpLX/uXJKlbZ8DTcS6f6/7ZGo1Gyf13XwcijNDXv1Wj0fTa1v2YnU6n8j734kPy81Nt6/493Le90lGNARV3eg7q7rNjedCsrhbag+PHRapidbVYysuFuM1muzKMgv6OwSC8JLIwMypKuPejosTAP2CA8Ka4Gz2nyu5QOTv6xxTuIiIPcKdT75/NNqd7r1zS91IcmzunmoF7eHic8+fKhsLF3FZFpT9xpkEkMFC45KOjRbqZ3d61yLFzWRDX0NBV4KamRhgScky8sVE8t9uvLsNBrxceCl9fcS7l0EhgoKj5EBoqBv/QUOHmN5m66yTcdRP9pcpnf+KqMwZUVFRUzgedTixymWp35EG9vb1rcRfEuQvn2tu7WjTL62UhndXaXawni/Jkg6Ozs+tvWch3qV36clplT/GiHK/vub6nvkHWNcidOz09uzQR7o9+fsJA8PUV7znHOY3KBaIaAyoqKioXiDxDldsonwlJ6lLTt7YKj0JTU5dRIKvtZfGiuwJfNiDk9e6ZDX0113G5Ti3c02h6pzf2FCHqdF0DtizUkxc5E8JdwCeLIn19uypIBgerNRiudFRjQEVFReVbRqPpyhoICxOVDi8GcmqfLDuSMyfceox1OwYQA7d7iMRgUPtuXI2oxoCKiorKdwS9XszIVVTOlSvCGJAr6n3++eds27aN2267rVv1PJmmpiZycnJ4++23OXnyJHq9ntjYWCZMmMDdd9/drRiQzWYjJyeHt956i5MnT2K1WgkICGD69Oncd999Z90o6MSJE2zbto2vv/6awsJCfHx8GDduHNOmTWP8+PHdxHrV1dUcPnyYjz76iLy8PLy8vEhLS2PChAncdNNNyvHV1tZy9OhR3nvvPU6cOIG/vz8jRoxgypQpTJs2rdcx2O123nvvPdra2pg0aRLDhw+/gLN9emw2G2vXrmXz5s0cPnwYi8XCb37zG0aMGEFwcPBZ7SMrK4ulS5eyfft2Ojo6eOqpp5g0aRKhoaGX7LivVDo6Ovjyyy956623aG9vZ/jw4cybN4/Vq1eTnZ2NXq8nIyOD66+/nilTpgDiesjPz2fTpk2sX7+eiooKPDw8CAsLY8iQIXzve98jNjZWFXGq9EKdzaucL5fdGGhra6O4uJjNmzezfPlympqamD17dq/tWlpaOHDgAB9++CE2m43JkyfT2dlJXV0dK1euJCAggOuuu06p6ldWVsZrr72G1WplyJAheHh40NzczMqVKwkODmb69OlEnKbzhCRJ1NXVsXTpUnJzczEYDMyaNYvm5mYOHjxIa2srNpuNWbNmAdDY2MiWLVv46quvsNvtzJgxg/b2diorK1m5ciVGo5EbbrgBl8vFvn37WL16Nc3NzUybNo3a2lqOHTtGfX09CQkJxMXFdTNqqqqq2LBhA9dff/0lL+izZ88evvnmG+rr65kwYQKHDx+msbHxjDUT3AkODmbIkCEUFxezdOlSamtrz+n93yV0Oh3x8fF4e3tz5MgRTCYTc+fOZfjw4QQHB7Njxw52796N1Wpl4sSJaLVaDh8+zMaNG9m4cSORkZGkp6crv4MdO3bQ2trKU089hZ+fX7+p6aByaiRJwmaz0d7ejtVqpbOzE5vNpjza7fZuj/Lf8vPOzs5uab7uTcbk506ns1e6s5z1JGcIyevc27XLGU4GgwGj0YjBYMDDw0NZDAaDsk7+22g04u3tjZeXF0ajUf2N9hMu63+po6ODoqIi9uzZw5o1a9i2bRvx8fF9Vu4rKSlh69atrFq1it/85jfcfPPNtLS0sH79el5//XX++9//MnHiRMxmMw0NDezbt48lS5bwm9/8hnnz5hEQEEBOTg6PP/44H374IfHx8QQHB582BfDIkSN8+eWX+Pn5cffddzN//nwKCgp44YUXut3APT09yc/PZ8OGDWzfvp2f/vSn3H777VRUVPDRRx+xcuVK3nvvPWbNmoXT6WT37t1s3ryZm266iSeeeIJDhw7x+uuvs3nzZmbNmkVsbKxSd6C5uZk9e/ZgsVgYM2YMMTExl/JfwsaNGzl27BhDhgzhRz/6EVu3bj3nRkryudVoNCxbtuycj0Gut5CXl6dUP+yvGAwGRo4cyZgxY8jNzUWv1xMZGcnUqVOx2+1oNBq+/PJL1q1bR0dHB15eXmzevJm1a9dSWVnJo48+yrhx42htbWXTpk189NFHvP3229x8880MHDiwzyqYKpcXuQ6H3W5XBnSHw6EMyHa7XWnyJa9ra2ujsbGR1tZWLBYLFouFjo6OXoaB/CgbBJ2dnXS4CQLkz+5pDMif545sDLhf23IVU/fnWq0WT09PPD09MRqNiiFgNBoVA8HdCDCZTAQEBODr64vJZMLT0xMPDw/0ej06nU5piS6v62lYyJ+r8u1yWY2B4uJiVq1aRXl5OTfeeCNlZWV9FsuRJInNmzezdetWoqOjefDBBwExA508eTIVFRX89re/VVzzJ06c4OOPPyYkJIR77rmHoKAg5Ud+zz338Otf/5p58+aRkJDQZx19ubDOf//7X9ra2pg9eza33norGo2GwYMHc9NNN/H++++zfv16CgsLSU9PZ+XKleTk5JCUlMT9998PQGpqKrNnz6apqYnXXntNCVfIPQJuuukmfH19mTRpEjt27OD48eNs2bKF6667DhDFg06cOMErr7zCX//6V8LDwy95d8MdO3ZgMBiIi4sjJCSEhQsXXtLP6wu5HPTDDz/MP/7xD4YMGfKtH8OlIjAwkIEDByr9KEaNGqWEBKqqqggPD2fjxo2UlZUxe/ZsrrnmGgACAgKYO3cuJpOJr776iuXLl2M2m1Vj4FtGvj/J94i+njscDsUrWFRURGVlJQ0NDTQ3N1NfX09tbS2NjY00NjbS1NREU1OT0gLcfRDU6/WYTCZ8fHzw9vbG29sbHx8fpU24n58fPj4++Pj4dCv4pdFo8HfLfdRoNHh4ePQyqiVJoqWlpdt3kA0TGdnL0N7eTmtrq2KsdHR00N7eTltbm2K8WCyWXudJo9FgMpkICwsjICAAf39/AgICCAkJITg4GLPZjNlsJjQ0lKioKCIjI5Vwqru3oufznudK5cK5rMZAWloaTz75JJIk0d7ezhtvvIHVau21ndPp5PDhw5SVlTFz5sxur4WHhyux1q+//pqQkBDKysrYuXMnc+fO7Tbz9/T0ZNasWTz//PPs3buXpKSkUzbVcTgcfP311wwdOpTExMRur40cOZI9e/awZ88eVq9eTWJiIrt27aKjo0M5FpnExETGjBnDq6++ypo1a0hISMBisaDX67u5/OWLura2VrmQNm3axPbt25kxYwaZmZnfSoxYHpAuhtFxvhdrY2MjH3/8MdXV1WddybG/Is+SQPzmcnNzqaurw8/Pj4EDB3bb1mw2M3jwYDw8PNi/fz8tLS2X45CvalwuFy0tLeTm5lJaWkpVVRXV1dWUl5dTXFxMeXk5dXV1tLW1KcaBr68vAQEBBAYGEhgYSFhYGDExMQQEBBAQEEBQUBAhISGEhoYqM2ofH59uM3TofT2d7vq6kIHydEVpe77WMzzR3t5Oc3MzdXV1NDU1KQZQdXW1Yvg0NjZy+PBh6urqqKmpwWKxKF4yWRsTGhpKeHg4kZGRxMXFERkZSWRkJFFRUcTHx3cLZahcHC6rMeBe7lYe6Pr6EVdVVdHwv84fYWFh3bbz9vYmKioKgJMnT1JcXExNTQ1Wq5XIyMhupXdlwaFOp6O8vFzputcTh8NBeXk5bW1t+Pv74+/v3+245IvWZrORm5tLWVkZzc3NGAwGzGZzt239/f0JDw9HkiTy8vJIT0/Hy8sLp9NJS0uLYozIlnVQUJDyXfbu3UttbS1PPfVUrxLCZ4MszPzss89Yu3YtFRUVWK1WvL29ycjI4L777iM+Pp7Ozk4OHjzIq6++ysmTJyksLOTYsWO88847eHp6smLFilNqFSRJoq2tjS+++ILVq1dTVVWF3W5Hp9MpMU0Zq9VKTk4Of/7zn6mtrcVqtaLVaomMjOTRRx9l6NChHD9+nC+++IIlS5ZQXl7OnXfeSXh4OMOHD+ell17i8OHD/Pvf/+bEiRO0tbXhcDiIiYnhjjvuYOTIkcpvoT8iSRKtra04HI4+Z3Ly9eLr64vFYjlt2WqV88fhcNDc3ExNTQ1lZWUUFRVRWFhIZWUl1dXVSoMwp9OJ0WjEx8eHwMBAoqOjGTx4sDLIy7NdPz+/bm7yni5z93V6vb5bifL+NPuVJAkPDw98fX0JDw/vFRpxOp3Kc3mx2+1KK/SamhrFi9LS0kJzczPFxcVkZWUpHgy9Xo/RaMRsNiue3fDwcMVIiI2NxcvL65J7UL+L9AtlR2NjI1arFZ1Oh8lk6vaaXq/H29sbvV5PfX09dXV1tLa2AmK27X4xyS4rDw8PxeXVF06nk/r6ehwOhyKCccfLy0tx89bV1VFfX4/NZlNcee4YDAbl+Orq6pQZgCRJ7Nq1i7i4OEpLSyksLMThcDBkyBBcLherV6/GbrczZsyY8x7g6uvr2b17N++//z6RkZFkZmbi4eFBe3s7+/fvx+FwcOutt5KamkpkZCQzZsxg9+7dmEwmBgwYwKhRo5RY3qmoqKhg165dvPfee4SEhDBixAh8fHyQJIkTJ04o7Y1B3GSrqqoAGDZsmNJhcevWrbz77rs89dRTmM1mUlNTldjjlClTSEhIIDY2FkDxFiQkJBASEoLdbmfdunUsWbIEDw+Pfm0MAIqLVI7z9kSSJOx2OyaTSb3hXQBybL2hoUG5b1RVVVFRUUFTUxOtra20tLTQ0tKi9AEB8PPzIzg4mJCQEPz9/TGZTJhMJvz8/LrN6n18fJR1RqPxqsj8cC+nfi7l2GXtQ0tLC01NTbS0tNDW1kZbWxutra3KOtlAaGhoULq2VlRUKLoGOewQGBiotImPiooiPDycgIAAvLy8+pVx9W3TL4yBtrY27HY7Wq2218Cs1WrR6/UYDAYlriWHGnpuK3f/8/DwwGq19hmSAOHukg0F2WJ3R7bktVqt8iN1Op19XgTy8ckGSFBQEGlpaRw5coTNmzcTFxdHTk4OVVVVREREMGLECEpKStizZw9jx45lwoQJlJeX09DQoMxE/Pz8CA8P75ZK2dd3KCsrY+nSpRw4cIA777yTcePG4e/vT3l5Oc8//zwff/wxsbGxREdHM2DAAAYMGMCbb75JZGQk8+bN4+GHHz7t/8XlcnHixAmWLl3K3r17+fOf/8y0adMIDw/HYrHw2Wef8cUXX3Q7/0ajkRkzZjBlyhRCQ0MpLS1VvAH33nsv48ePZ9q0aXz55ZdYrVYefPBBJZXS5XJhMBgYO3YsKSkpDBo0CJvNxuHDh/nmm28YNmwYs2fP7tfqZXnwsFgsNDc3d3vNbrfT3t5OR0cHYWFhqqv0LJHV+h0dHVitVjo6OpTzW1JSQllZGeXl5ZSXl1NSUkJrayuSJCmz/vj4eOLi4ggPDyciIoKYmBhFUyOr8VXOH1lA6OPjc8qwrdPppKmpqZu3prS0tJu3Ji8vD6fTiclkIigoiPDwcOLj40lKSiIqKoqgoCBMJpMy0fDy8uoloLya6Rd3TYfDgcvlOmWDHNkilVW2ctvfU1njOp2uz1QbGXn2JUnSKd3zcve/zs5OZduzOT6dTse1115LcHAwf/3rX1m0aBHBwcFMnTqVefPmkZ6ezq9+9SvS0tIYPnw4DoeDf/7znyxfvpy2tjZiYmKYMmUKP/7xj5XZcl/Y7XYKCwtZvnw5c+fOZe7cuYSEhAAidPH0009z7bXXsmPHDuLi4k67r9N9Rm5uLl999RVjx47ltttuUwRtLperV3jFZDIxY8aMbvsIDQ1l0qRJbNy4kaampm7K6J5otdpedRgcDgeTJk1i//79yozuUqdfXio0Gg0JCQkEBwdTV1en3Nzk35ScgupwOEhPT+/lJVPpW+DncrkoKSkhJyeHvLw88vLyOHLkCNnZ2dhsNoxGI8HBwWRkZHDNNdeQnJxMbGysYiifT4hO5eKi0+kIDg4mODiYAQMGdHvN5XLR2dlJbm4uJ0+epKCggIKCAnJyclixYgVWqxUPDw9CQkKU+2pGRgbp6enExMTg5+en3Lt7ihWvJvqFMSBbb2dynRqNRjw9PdHr9YobsC/sdrsyW+8L2QPh3ia4J3L8Sw4ZaLXaU7Yflo/P09MTjUZDREQECxYs4LrrrlNuXlqtlvb2dnbv3s2OHTt4++23qa2t5Z133uGTTz7h888/JywsjMWLF7Nu3Toef/xxPv/881P+aKuqqigqKqKzs5Phw4d381jo9XoyMzMxmUwUFRVx8uTJPvdxJqqqqqisrARgypQp5zUjl0VDQK+20meL7CGRb/z9Gbkexaeffsry5csZP348c+bMobGxkVWrVvHuu+8SERHBPffcoxh3Kt1pbGzk2LFjHD9+nOzsbHbs2MGhQ4eQJAl/f3+io6MZOHAgd955J0OGDCEhIYHw8PBeg8HVOCD0RzQaDZ6enmRkZJCRkdHNEJQnRQUFBZw4cUJJF3/11VeV0G50dDSTJ09m/PjxjBw5kri4uF7izauBfmEMBAYGYjQacTqd3dJXQAzKcqEOWZXb3NysCNvcBxdJkrBardhsNiWu1xdy/Emj0SjbuyPn+7pcLiVFRq/XY7fbex2fw+FQ9hEcHNwtbcbdiyD/aP/617/y9NNPExkZya5du8jPz2fUqFEMGjQIg8HAuHHjqKqqYtmyZVgsFry9vfv0RsiuUBAz8p7aCVk/4XK5ThkuORNyLM/9fJ0Oi8VCVlYWH3/8MSUlJbS0tNDe3n7WWQMul4udO3eyatUq8vPzqa+vx2KxUFFRQW1t7Xl9h0uNXIHwww8/pKSkhPb2dh5++GF+97vfceDAAf773/+yc+dOGhoaeOyxx/j73//OtGnTCA4O5ssvv+SNN97g5ZdfVnK9Bw4cyPPPP09QUNBV796UU+HkG/3x48c5ePAgx48fp729HS8vL8LDwxk9ejT33HMPiYmJBAcHKwVxZJexXDRHHfz7J6dKNZQFh0lJSURHRzN69Gg6Ojpoa2ujoaGhm5Gwd+9e1q9fr6RBpqWlkZmZyfDhw4mLi+vl5fwu0i+MgZCQEHx8fHC5XDQ2NnZ7rbOzk4aGBiRJIiIigvDwcOrr69Hr9cp6GafTSV1dHU6nk5CQkFO6k/V6PaGhoRiNRtra2mh3bwuGGATb2trQ6XRER0cTGhqKt7c3NputV5zXYrHQ2NiIJElER0ef0huRn59PVlYWwcHBjBw5Em9vb5qbm2lublYEdVqtFrPZTEhIiCJwkr0SPZGFPMApB1qHw4G3t7cihjxXzmUmb7PZKC0t5Y033qCpqYnExET8/f2x2WwcOHCAioqK075fjhm+9dZbNDY2Ki4/nU7H7t27aWpqOq/vcKmRKxDee++9WCwWPDw8CA0NxWQyERMTw7XXXktmZiZOp1MRnAUHB5OZmYmXlxcDBw6kubkZnU6Hv78/MTExjBs37qodvOQsnPLycgoKCjh+/DhFRUWKYQginVe+F0RGRiox/tDQUFVpfhUhXx9ywST32gtWq5WkpCRqamqoqKigqKiI8vJyamtraWtro7KykhUrVrB161aioqKIjo5W9AcxMTHfyeuvXxgD/v7+ilhHHjTkGH1bWxslJSUAJCcnEx4eTlVVFYGBgZSWluJ0OpVtHQ4HhYWFOJ1ORWXaF1qtlsDAQEJDQ2lubqapqUnZB6CkvxiNRtLS0ggKCsJsNivpiu4FNxobGxXFa2pqap8Db1tbGwcPHiQ7O5tZs2ZhNpvRarVKKMLdgJDFiyAG2FMNxiaTCV9fXzQaDQ0NDd3c53JdB4vFQlRUFAEBAef4HxHIhoQkSco5OhXt7e2UlZXxySefcP/993P77beTlpZGZ2cnixcv5ptvvjntZ9ntdioqKliyZAnz589nzpw5TJkyBV9fX15//XWOHDlyXt+hCytgQ5Jc2GxeGAweF8VVbDAYGDVqFKNGjer1WmBgIMOGDevzfeHh4YSHh/fZq+JqQp79y0rzpqYmysrKOHbsGDk5OWRnZ+N0OvH19SUqKoqhQ4eSmZnJgAEDMJvNV6W7V+XMeHp6EhUVRVRUlCJQbmpqorCwkJMnT5KTk8PevXvJysoiKyuLwMBAkpKSGDZsGJmZmQQHByuZI77/6wzV342DfmEM6HQ60tPTOXbsGIcPH8Zut+Ph4YHL5aK2tpbdu3ej0+kYN24cYWFhhIeHk5mZyZ49e+jo6FD+WVarla1btyJJEkOGDCE1NVXRFsjZALIwUKfTMWHCBPbu3UtJSQkOh0PRIhw+fJiioiL8/PyYPHkyHh4eDBs2jPr6eo4dO9bt+IqLizl48CAeHh5MnjxZORboEjsdPXqU7Oxs7HY7t9xyi/KjktOW5IFWDn00NTUprvlTzXLMZjPR0dF4eXmRnZ2N1WpVjAO73c7BgweVWgznW+I4PDycsLAwHA4H+/btw2azKerqnpoNWbntdDqZP38+6enpBAQEUFdXd0ojwj3219nZSUFBAXa7nalTpzJ69GhCQ0NxOBznpTPoTQlQjMtlpagolejoqG4hnb5iyf394r+ScblcSvW7pqYmNm3axNq1a9mzZw8VFRVotVpGjBjBbbfdxqRJk0hJSTnrRloqKn0REBDA8OHDGT58uHJPycnJYdeuXezatYudO3fy3nvvKd7b0aNHM27cOMaOHYvBYFDGjv56X+gXxgDAvHnzsFqtvPDCC/zhD3/gkUceobGxkdWrV/P+++9z5513KrXaBw0axEMPPcSXX37JX//6V+644w5CQkI4cOAAr7zyCtdffz2jRo0iNDSUpqYmXnnlFd566y2eeOIJ5s2bx4ABA9BoNPzkJz/h0UcfZePGjYSEhPD973+fnJwc/vOf/9De3s6NN95ISkoKAHfeeSfNzc189tln/PGPf+Txxx+nuLiYzz//nHXr1nHHHXeQmpraS2TX3t7OX/7yF0aNGsVDDz3U7Yc0cOBAjh8/zpIlSzh06BBhYWFs2rSJvXv3Mm3atF5aAHf0ej1paWncc889/OMf/2Dy5MlMnz6d4OBgCgsLeeKJJzAYDMyZM+e8Z59+fn4MGjSICRMmsGzZMqZMmcKsWbMwmUzk5uby7rvvKnoLHx8fxROzZcsWEhMTcTgc5OXlkZWV1c1zodPpCAwMpLq6mtLSUqKiotBqtYSHh6PRaMjKyiI5OZmgoCDKy8vZt29fL63GubMHeJ+2tg1kZGjw9vYmIiJCcQ+mpqaSnJxMXFwcERERvYpfqVxcsrOzWbduHRs3blS6Xw4bNox58+YpN+Ceoj8VlYvNgAEDSE9P55577sHhcFBUVMSGDRvYtm0bH3zwAb/73e/w9/fnuuuuY/78+YwYMYLo6OjLfdjnhUY6j2lVbi48/TQsWwbyu9PT4fe/hxtugLOtr5Gbm6tY+9XV1ezatQuXy0VKSopSXer+++9n8ODBABQWFvL111+zbNky2tra0Gq1+Pn5kZyczGOPPUZiYiJ6vV6pIPbFF1+wbNkympqacDqdeHp6kpCQwOOPP05SUhI+Pj40NDTwt7/9jX/96188+eSTzJ8/nwEDBihiww0bNrBlyxaysrJoa2vDw8OD6Ohopk6dyty5c4mPjweEduHIkSN88803rF27VtEUhISEMGjQIO677z6Sk5O73bQcDgcvvvgiDoeDKVOmMH78+G6q/+bmZo4cOcJbb71FUVERLpcLo9FIamoqd999N2PGjDntTbCjo4OKigr+8Y9/cPToUdra2nA6nUop5O9973uMHz9eKeH829/+lhUrViiVGseMGcOtt97KpEmTTllEpKGhgezsbP72t79RWFiIwWBQQiwhISG89tprpKenc8MNNzBt2jT+85//sG/fPvz8/IiOjiYuLo6goCCee+45RowYwZ133sl1113HsWPHeOSRR/D391dceQ888AAvvPAChw4dAkRaYlpaGqGhobz66qv4+fkxZ84cnnnmGUJDQ884QLS2gr+//BuuB2rw8qrijTfKKCsro76+XhFJVlVVUVtbq9RKkCtfxsbGKvHpiIgIIiIiiIuLO6WWQ6Vv5HDfjh07WLt2LXl5eXh6ehIdHU1GRgaTJk0iPDy8Wz3+U+lvVFQuBXK9ipaWFlpbW6mrq6OwsJBvvvmG3bt3Y7PZMJvNDBkyhDlz5jB58mS8vb37jUblsnoG/P39GThwIF5eXrS2tjJ58mQA5WYri6nkwj0xMTHMnj0bo9FIbW0ter2e4OBgkpKSSExMVE66Xq/H39+fWbNmYTAYqKmpweFwYDKZSE9PJzU1VSnY4uXlxfTp0/Hz81MGRujq3jVq1Cj8/f1JSkqirq4OLy8vEhMTGTBgQDf3utFoJCUlBU9PTwICAqipqcFgMBAeHk5ycjIJCQm9vr9Go2HYsGEEBQURHx/fa8D19fVlwIAB3HHHHeTk5GC32wkMDCQ+Pp5Bgwad8fx6enoSGxvLokWLGDBgAE1NTTgcDoxGIzExMUycOFHJAvDz82PKlCmKIeTl5UVISIhS0vl0/8MhQ4Zw3333kZ+fj81mw8vLi+DgYKKioggODsZoNDJw4EASExP53ve+R0ZGBk6nUynVHBERgcvlwmQyMWjQIPz9/Rk6dChPPvkkbW1teHt7ExsbS3BwMHfeeSeZmZm0t7djNBqVAdnLywu73U5MTMx5xomDgUD0+gSmT2+gsVE0lnG/8Ovq6mhublaqVzqdTkpKSiguLgZQjNPQ0FD8/PwUQaCccRIYGIi/v/8V1XpYzsbx9vb+1mfXTqeTiooKsrKyyM3NpaioiLKyMpxOJ2PGjCE5OZnExESSk5NJS0tTK8ipXFbkiYDcXEkugRwWFsagQYPIy8ujqqqKEydOUF5ezu7du5XxJiUlpZuA8UrksnoGVFQuJ909AwJ/f2ho6Ps3bLfbaWhoUGqoFxcXU1JSolRBq66upqOjA4fDgaenJ35+fkpZVFnLIussfHx8lJawcvtXWfX8bcUd5a51+/btIzk5WcmgudQejY6ODlpaWqipqWHPnj2sW7eOwsJCbDYbYWFhzJgxg9mzZ5OYmIifn98lPRYVlQtF1jXl5+dz6NAhdu7cyfbt22lvbycxMZFhw4Yxbtw40tLSCA8Px9PT84r0FqjGgMpVy7kaA6dDroJWWFhIfn4+xcXFlJaWUlxcTF5eHgUFBUr2h4eHBxEREaSkpBAXF0d0dDQxMTGkpKQoaaTujWoulXDR6XSyb98+5syZw7333st9991HYmJit9oVF+Oz5FuMXJTr2LFjbN68mU8//ZRdu3aRmZmpaFfGjh2rFOdSUemPyCnsS5cuZfny5YqA+5prruFHP/oRqamp+Pn5KYLDKwXVGFC5armYxoB7GVz50b3fvc1mo7KyUql/X1paSl5eHsXFxUptfDkLxWw2ExERQWxsrGIgJCQkEBMTQ3h4+EVLl6urq+Obb75h0aJFeHh4KOWib7rpJm688caLVndfrgS3fv16Xn/9dQ4cOIDdbicjI4NHH32UmTNnKgaIKgZU6e/0NH4LCwvZtGkT//jHPzh58iSZmZnMnj2bG264gaFDh17mo+1CNQZUrloupjFwJmTxkdzWubOzU6lMabVaaW9vp7a2VmnBXV9fr4Qk5E6Per0eT09PgoODu7VvlbMekpOTz6moTl5eHqtXr+aJJ54AhObB39+f0NBQEhIS+MEPfsCMGTPw9/c/7xlMWVkZe/bsYcmSJWRlZZGYmMi4cePIzMwkPT1d0VdcSTMkFZWLhbvosKqqig0bNrB582alNs6MGTN45JFHiIiIOO/ibxeLK0PFpKLyHUcWH/XVaVAurNPW1kZdXR1NTU1K9UnZIJCft7S00NnZSVVVlWIk6HQ6fH19CQsLUwSKAQEBBAYGKi1dAwMD8fX17ZWtUl1drTTukit8trS0UFlZiU6n4/jx44wcOZJBgwaddT0KSZJobW1l27Zt7Nmzh2PHjlFVVcXs2bMZPXo0Q4YMIS4uTq0LoPKdR77uZRGxLN7Ozs4mJyeHnTt3YrPZGDduHBkZGSQnJ1+2LBnVGFBRuczIzZrkQdsduflSQ0MDtbW1VFVVUVpaSlFRERUVFd3at1qtVkW4KBsC7imPYWFh+Pr64uXlhdFoVNT7crVLGbnk76pVq9i/fz/Tpk1j1qxZjB07VsnW6GsmLxeHqq2t5ejRo3z00Ufk5eWh1+sZO3YsjzzyCHFxcVdkSqDD4VDOr8PhwNfXF7PZfEkNFjl9ubq6WqkSGhISopRfP9N75VLsTU1NpKSkXJHnVUUgh79SU1NJTU1lwoQJ7N+/n3//+9+sXbuWsrIyKioqmDJlCvHx8QQGBn7r4TI1TKBy1fJthgkuFXKjKblEryxcLCkpobCwUDESXC6Xkuoq1/Goq6tTOvudqtujVqtFkiR8fX0ZPXo0L7zwAoMGDeoW44cud+jJkyf5+OOP+fvf/05ISAiLFi1iwYIFjB079ts8LedMfX09//3vf/n73/9OZWUlkydP5oEHHuCWW265ZJ9ps9nIzc3ln//8J6tWraKlpYW7776bu+66i9GjR5/yfbIn6cSJE3z++ed8+eWXfPHFF6csr36uyFoXuZS7e2dVd2Hr6ZA9TT1/V3KDNjmU5f77kWPs7uXcT7WtfHzu+5dT0N23vZKRtTRvvfUW//3vfykrKyMqKoqf/vSnLFiwQBHSflvfRfUMqKj0Y+R6GCkpKSQnJ/cSLjocDkW4WFpaSkFBAXl5eRw9epTS0lJqampO2/ZZfq21tZUtW7YwefJkZs6cye23387MmTOJiIgARL+Od955h88++4yysjJ+9KMf8eCDDxIWFtYvZqxBQUH8+Mc/5tprr+Xuu+/+Vj5z27ZtrFixgl27drF3717WrVuneAtOR3FxMf/+979ZtmwZubm5Sm2Qi8k333zDe++9x65du6isrMTX15f58+dz//33M3DgwD7DXe688847rF+/nkOHDinN4cxmM2lpaYwfP557772XyMhIZXs5rPR///d/5OXlKY3nZs2axa9//WtMJpNiFLhcLjZs2MDrr7/OoUOHaGpqws/PjwULFvDEE08QFRXVL35zINqvP/zww9x2223s3LmTTz75hHvvvZcZM2bw85//nMzMzG8tvVY1BlRU+jE9W1O7I7dwlTtrDho0SBEudnR08K9//Yt//etfZ/U58swfUFprf/zxx1x77bVER0fz5ptvUlNTQ0pKCi+88AKZmZmEhoai1+v7xSxNo9EoTcC+rePNz8/n+PHjSrOzefPmIUnSKat9Op1OTp48yeLFizEYDEyfPp3Ozs6LagjYbDays7P5xS9+QXV1NWPHjuW3v/2tUs21traWm2+++Ywek9zcXFwuFwsWLGD+/Pm0tLTw5ptvkp2dzdGjR9myZQsffvghAQEB1NbWsnPnTp5++mmam5v5zW9+Q3Nzs1Ly12az8fOf/5zo6Gja29vJzs7miSeeoLm5mRtvvJHMzExWr17Nxx9/TEtLC/fccw9Tp069aOfkUiH/znQ6HQEBAUyaNImkpCQmTJjAa6+9xi9/+UtmzJjBokWLGDJkyCU/notmDFitUFAA+/dDP7j2VVS44HYGVzjyzcZgMPQaYJqamvDy8uqmFThbWlpaaGlpob6+npaWFgIDAykrK2PcuHHMnj2bCRMmXJaYZ3+jqamJ+vp6oqOj0el0BAUFnXZ7uVLo6NGjCQwMpKioiG3btp2yRfn50NHRwcqVKzl27BiRkZGkp6czceJEmpubWbFiBTt27FCql7rP7PvaT1hYGNOnT2fUqFFYrVaOHz9OXV0du3btoqWlhWPHjjFs2DDy8vJYt24dJ06cYNiwYYwcOVIR1K5Zs4a1a9dyww034OfnR3NzM19++SW5ubmkp6czZMgQJk6cSHt7O0uXLmXTpk0MHTqUgQMHEhoaetHOy6VGblEud4Jtb2/nm2++Ye/evbS0tLBo0SLGjh17SYsVXTRjoLUVdu0S8Vb1HqDSH+js7K4XuJqora2lpaXltCECufuk+3ODwYDJZMJms9HW1sbOnTuJiYlhypQpLFq0SOni+V1DzpBoaGigsbERh8OBwWDA399fKTMNovFYfX09jY2N2O12ZaYfFBREYGAgJpOJlpYWysvLKSoqor6+nvr6evbs2YNOpyM2NlZpgtUTuVnX/PnzlXU6ne6UxoCcFeJwOAgODj6jpkBubb5y5UqsVqvSpCskJITx48ej0+koLy/n2LFj5OXlndYYkEVww4cPVyprjhgxgoMHD7Jjxw5aWlrIy8sjJSWF3NxctmzZgiRJDBgwQBFQDhkyRKnsd/ToUWJiYmhvb2fVqlU4nU4SExOJiooiIiJC6dNSXFxMTk4OBQUF/coYkPHw8CA+Pp6HHnoIX19fvvrqKzZu3IgkSZjNZmJiYi5ZUa6LZgzU18Nnn12svamoqFxKysvLaWxs7PM1WbhlMBgUQZdsCMTGxpKQkEBNTQ0VFRU4nU6eeeYZbrnlliu+9vqFYLfb2bt3L5999hlr1qyhoaGBiIgIpk2bxvXXX8/cuXPRaDTk5uby0UcfsWLFCurr67HZbERERDB//nwWLlzImDFj2LdvH3/4wx84ePAgjY2NFBcXs3r1ary8vHjxxRd56KGHLsoxZ2Vl8ec//5na2lruuOMOHn/88dMOInKDt/379yuZDYGBgej1euLj45VZaWVlJXv37j2tK/7nP/95r3VGo7Gboehyuejo6KC0tJTjx48DEBUVpTQ7czc29u/fr3R9PXjwIICSHePp6UlcXJyy7cmTJzly5MgVL1o9FRqNBh8fHx544AFGjhzJ0qVLefnll2lqauJnP/sZaWlplyT8pmoGVFSuQoqLi6mrq1MGfjlc4OfnR1hYGKmpqQwaNIiUlBTi4+OJjo4mOjqaxsZGHnroIRobGxk+fDh//OMfSU9P/84XDXIXR7744otMnjyZTz75hC+++ILt27cTHh7OsGHDaGxsJD4+nscff5ybbroJSZJ44oknWLFiBcePH2fFihVMmzaNyZMn88ILL7BkyRImTpzIP/7xj27K+YuBy+VSxKSn8wDJtLS0KN1RAaV3hkYjWnrLA1BTUxMFBQXnfDxlZWXU1dUBXU3aGhsbqaurw+VyodVq8fHxQafTKQW25BoYRUVF5OXl4efnpxyft7c3BoMBrVarHJ/NZqOmpkYp6tPfGTZsGImJiYwYMYIHH3yQ8vJyvve973HHHXdcdA+cagyoqFyFeHh4MHToUKXrY2JiItHR0d3ilvIiNy9qa2vjpz/9KeXl5dxyyy0sXLiQpKSkb62x0uVAFk6uX7+exsZGpkyZwqxZs/D19eWmm26ipKSEpUuXsnLlSjIyMhg1ahRDhgxBo9EoNSPGjBlDcXGxUhMgICAADw8P5bzpdDplULuYjBo1ildeeQWHw0FQUNAZ/0d2u522tjbluV6vV1IJ5XCHRqPBZrPR2tp61schh5p27tzJ8ePH8fHxYeLEiaSkpHDixAk6OzuBrnobcjqdVqvFaDRisVhob2+npaWlm7Gk1+u7/faMRiN2u12p6Nnfkc+Dr68v06dP529/+xtvvPEGH3zwAY2NjTz22GMX9do7L2PA0xMSEyEz8+qNuap8N/HxuTo0LxkZGURGRioDhdlsJigoCKPR2Ku9ssvloqKigk8//ZQjR46wYMEC5syZw8CBAy97CdVvg7q6OiorK/Hw8CApKUkZ5MPDwwkNDUWr1ZKbmwugtK52x9/fXxnU7Hb7t3bcfR3L6ZBz92V65rjLz8/W0+DOzp07OXjwIBaLhUGDBnHPPffg4+PTa189Bzb5udPp7FVX4FTb9lXfoD8jZxtMnz6d0tJSdu/ezdq1a0lPT2fWrFkXzZt0XsaAjw+MHQs9iqWpqPR7zpA+/Z1h8ODBZ71tY2MjBw8e5P333yc2NpZbb72VtLQ0vL29L+ERXjnU1tbS0dGBVqulubmZ3bt3K69VV1cr28giw6amJqVstNPppLi4mNbWVrRa7RU9SMleChn3ehXQFXboud3pcDgcNDY2snTpUiorK4mNjWX27NksWLBACYu4e0TcP8/dUNDr9YonoK9tAcWQudjhlisBjUZDdHQ0N998M06nk+XLl/PBBx8wbNgwgoODL0rI4LyMgeBguPnmC/5sFRWVKxyXy8XmzZv58MMPqaio4N133yU1NfWMRWe+S8gVHA8fPsyRI0f485//3O11k8mkCC2zsrJYs2aN0oymtbUVm82Gw+FgxIgRl+kbnB0Gg6GbJ8HhcHSrQmiz2XC5XBiNRnx9fc+4P5fLRUtLC6tXr+bNN99k6tSp3HXXXUpHTAAfHx/FuyRX5HMvnCWHEHx8fPD39+9Wprmv45MkCS8vrzOWc+6vpKWlsWjRIry8vHj22WeZNm0a1113HWaz+YLDBapmQEVF5ZTs3buXpUuXkpOTw9/+9jcGDhz4nRcL9kSeeU2bNo27776b22+/vc/tqqur+dnPfobD4WD69OksXryYsLAwlixZwgcffIDlCi9s4evrq2QNOJ1OOjo6sFqtSJKExWJRUhgDAgJISko64/7y8vL48ssv+e1vf8uzzz7L7bffTnR0dLffT3R0NCEhIeh0OsWz4nQ6sdvtdHR0KJ6B+Ph4JZtAPr729nbFQLFYLIpnIDQ09KybavVH0tPT8fHxoaCggGeeeYaIiAgmTpx4Vgba6bi6rmoVFZWzwr1uelVVFdOmTWP27NnfabHgqQgLC8Pb25vW1lbKysqUIk7ui16v58SJE7S2tpKens61115LXFyc0imyPxhQer0ePz8/RowYgV6vp66ujoaGBhwOBwUFBcrAHBERwciRI3G5XPz2t79lwYIF/PCHP2TVqlXKvnbs2MHixYtZu3Ytf/nLX7j77ruJiIhQBIkyRqOR2NhYpaRyaWkpNpuNxsZGysvLle0yMzNJTEwkKCiIzMxMAKqqqmhtbcVqtVJUVKRsm5SUREZGxiU+W5cPjUZDaGgoP/7xj/H19WXJkiVs27btgveregZUVFR6IUkS+/fv5/Dhw6SlpTF37twzVsj7ruLt7c3AgQM5cOAAe/bsYe/evQwePBiDwYDdblfKNMuu787OTiwWC1qtlsbGRioqKmhtbf3WjaiCggJ27NhBe3s7GRkZjB079rTHoNFoMJlMLFiwgOzsbEpLSykuLqa2tpYdO3bgcDiIiopiwIABJCcn09zczKpVq8jPzyciIgKj0ci1115LfX09y5cvZ926dVRVVbFt2zZKS0t7iRFTU1OZM2cOaWlpTJkyhWPHjnHs2DFqa2upqKjg8OHDaDQaUlJSGDRoEKGhoXh6ejJv3jz2799PQUEB5eXlVFRUsGvXLiRJIj4+noEDB5KQkPBtnOLLhsFgICEhgWuuuYZ9+/Zx8OBBRo8efUFdNlVjQEVFpRdOp5OvvvoKq9XK4MGDGT9+/OU+pEuK3ML42LFjiju/uLiYEydOkJSUxKRJk2hsbCQvL48VK1bQ0tKiqOF1Oh3h4eGEhYURERFBY2Mju3fvJjg4GKvVSl1dndJD4Pjx43h7e1NZWUlNTQ1Wq5Xa2lqys7MJCwvDz8/vlBkacoXAiooKrFYreXl5tLW1YbfbOXr0KHV1dfj4+CgV/IqKivjwww+pq6vjjjvuOKsiPJ6enlx33XUsX76ciooKjh07xpYtW9i4cSNBQUFMmDCBMWPGEBYWRk1NDVVVVVgsFpqampSZfG1tLZs3b1aKA7333nu9Pkej0TBv3jxGjBhBSkoKs2bNYuPGjVRWVrJnzx6am5s5dOgQ4eHhzJ07l5SUFPz8/NDpdMybN4+lS5fS0NDA4cOH0el0bNmyhdDQUKZPn86wYcMwm83n+UvoH2g0GoxGIzfffDM7d+4kNzeX/Pz8C2u5LamoqKi44XK5JIvFIqWlpUnf+973pHXr1l3uQ7rk1NfXS3/729+kxMREycfHRzKZTFJMTIx02223SVarVbLZbNKGDRukRx99VEpKSpJMJpMUFhYmjRs3TnriiSekY8eOSS6XS3rttdekuXPnSuHh4VJ4eLh02223SS+99JJ07bXXSn5+flJGRoa0ZcsWacGCBVJ0dLRkNBolHx8facyYMdJbb70lnThx4pTHaLfbpV27dkkLFy6UBg8eLIWFhUkGg0EyGAxSVFSUlJmZKT3yyCPSzp07JZfLJW3atEmaP3++NH78eOnvf/+75HK5zupcuFwuacOGDdLdd98tpaamSn5+flJMTIz06KOPSvv375esVqvkcrkkp9Mpff/735dSUlKkuXPnSosXL5ZcLpe0f/9+ady4ccqx9bV4enpKCxculI4fPy5JkiQ1NTVJq1atksaPHy+ZzWbJbDZLGRkZ0v/7f/9Pam5ulhwOh3J8DodD+uqrr6T58+dL8fHxUkBAgBQfHy89/vjjUmFhoWSz2S7sx9BPcLlcksvlku655x5p9uzZ0rPPPqusOx80kqRWClBRUemira2NgwcPMm3aNN59912mT5+utCr+riL9L42tZ+Mm9zQ1eZueue7uqXby6/JtVS6e0zMtT1bBu+9Hq9WeVpMh/U813/O9PY9F3o/79zmXdDv5c3p+176OT879d//c0x1jz+OVqxqe6ru5H7f8ueey7XcZ+bsvWbKETz75hJqaGtatW4eXl9d5fX81TKCiotKN1tZWdu/eTVhYGOnp6YSEhFzuQ7rkyIP06QZMecA7HafaR8915yModB/szwZ5cL6Un9OzQJX8/nP93HP5zHM9D99V5AF/1KhR7Ny5k2PHjnHkyBFGjRp1XsbA1X02VVRUetHR0UFubi6JiYn4+/t/J7sQqqh8VwgPD8dsNqPT6cjLyzujR+ZUqMaAiopKN6xWK8XFxSQmJioKeRUVlSsTb29vgoKCMJlMFBUVqcaAiorKxcFut1NbW0tUVJTqFVBR6Qf4+voSFBREdXW1agyoqKhcHJxOJ62trUo7WRUVlSsbT09PvL29aW5uPu99qAJCFRWVbmi1Wjw9PZWa/P2V48ePs2bNGj744IM+X09ISODXv/41Q4YMAYQ6+8iRI3z66adK3r7JZGLu3LnceOONREVFnZVxVFlZyaFDh/jkk0/Iz8/Hy8uLgQMHMmHCBG644QalTS+IzI1jx47xr3/9i4KCAjQaDREREUyaNIn77rtPaRvc1tbGyZMnefXVVykoKMBoNJKUlMSsWbOYP39+n4Kxjz/+mPr6euLj45k3b94FnMnTY7fbOXjwIKtXr2b79u1YLBYeeughJk2aRHx8/Fnto7KykrfeeosNGzbQ0dHBjTfeyO23305cXNwlO+7vEnLxqwsR+6rGgIqKSjd0Oh1+fn40NTX1SrXrT1gsFkpLS8nKyurz9fb2dtra2gBRdKiuro433niDtrY2oqOjGTJkiNJoR6/XM3Xq1DN2e2xoaGDTpk1s2LABp9PJzJkzaWtro7y8nJUrV+Lh4cGCBQuUAT47O5u3334bm83G+PHjcTgcNDQ0sGLFCoKDg7nmmmvw9vampKSExYsXU19fz9ixY7FYLNTX1/P++++TkZFBTEyMoux3uVzU1NSwefNmBg8eTGRk5MU9sT3Izs5my5YtHD58mMmTJ3Po0CEsFotybs8GLy8vhg4dSn5+PqtXr6a4uFhpUqRyZqxWK+3t7fj5+Z13WqVqDKioqHTDw8MDs9lMVVWV0pymv+Lh4aHUtO+ZihYfH690t7NarRw8eJBPP/2U++67j+uuu46MjAwKCwu5//77WbFiBd7e3qSkpJy2Y2NeXh5ff/01e/fu5fHHH+f222+nrKyMxYsXs27dOj744ANmz56Nl5cX5eXl7Nixg2XLlvHCCy9www03YLPZ2L59O3/84x9ZvHgxI0eOJDg4mPz8fD799FPuueceHnroIerq6lixYgWvvPIKBw4cIOz/t3fnwVGX9wPH37ubPZNNNtndbBKSDeQkEAgBEwhHIByR1spIlSLFqp1WrfWqY48ptR21YnGmrQq2tQ5YpwKiow7V8QKpHJUQCBAIR4CEkIRsQo7N5tj7+v3h7PdHhKCCSEKe18wOJNn5fne/C3k+3+f5PJ+PxUJUVJTUvW/Pnj309vaSm5v7tdpVX45IieZgMMjDDz/M9u3b0Wg0X6uzpcFg4Oabb8br9bJ79+6v/RoitQdOnz5NfHz8lVXiG2Yiy3q9vb0kJiZe9nFEMCAIwgA6nY7c3Fw++OAD3G63VCxnOIqPj+fJJ5/kBz/4waA7I8LhMA6Hg3Xr1hEXF8fixYuZOnUqMpmMiRMncuedd/L888+zZ88eZs+efdGOfZGkrc2bN3Py5ElycnL4yU9+AiD1dujt7WXdunXU19eTk5NDZWUlW7ZsISkpiZ/+9KdSE59wOMzSpUt5/PHH+fnPf47ZbKaurg6Hw8GSJUtITEwkNTWVzs5O1q9fz44dOygrK0On0xEKhXA4HPzpT39i5cqVFBYWXvUk0EOHDtHV1UVBQQGxsbEsWrToqp5vMC6Xi9/97ncsXryY22+//Zq8hmvB6XTS0dGBw+EgLy/vsv+vigRCQRAG0Ov1lJSUUFtbS0NDA729vdf6JV1V4XCY3t5etmzZwtSpUzEYDNLPZDIZc+fOJS4ujrq6Onbt2jXocYLBoNTQp6CgYMDPsrOzKSoqIhAI8MEHH+BwODh+/DgnTpygpKRkwKyF0Whk7ty5AGzfvp0jR47Q09ODTCYjPj5eeq5KpSIhIYH29nZpOefkyZO89NJLzJs3jxtuuOFbaS7V1dVFX1/fRQsQfV2XO5AFAgFee+01GhoacLvdV/w6hpPKykrq6uqIjY1l2rRpl12MScwMCIIwgF6vp7i4mPj4eP73v/+RmJjIlClTrvXLumr6+/tpbW3F6XRisVikpL2ItLQ01Go1drudxsbGQY/T0tJCb28vcXFxmEymAccwGAxYLBZCoRAnT56kubkZu91OKBSSSj1Hnq/RaEhNTQWgoaGBrKws9Hq9FLRYLBbg86Qxh8OB0WhELpdjs9morq7m4MGDrF69mpiYmK89uEZmON566y127NhBfX09PT09qNVqCgoKWLx4Mfn5+URHR1NdXc1zzz3Hrl276O3tpbGxkU8++QSNRsPq1aulVsMXO0coFGLTpk18+umnNDU10d/fj1KpxO/3c+7cOem5oVCIw4cPs3btWurr63E6nQSDQVJTU7njjjuYMmUKoVCI9evXs3btWtra2njqqadYu3YtVquVDRs2UFtby7vvvktFRQUOhwO/309ycjI33ngjpaWljB079mtdo6Fm165d9PX1UVhYOCCQ/bpEMCAIwgAKhYKEhASmTZvG/v37GT9+PPn5+V9rDXiocLvdbN26VSrGEhUVRXR0NGazmenTp2OxWPB4PDgcDkKh0EW3U0ZHR6NSqXA4HHR3d1/0POFwGLvdjs/nQ6lUotPpBvxcqVSi1WqJioqis7OTzs5OXC4XMplMyluIkMvl6HQ6lEoldrsdrVaLTqdDp9Oxe/duLBYLTqeTkydP0tPTw8SJE1EoFFRUVFBbW0tpaSmpqamXdYcYWbN//fXXiYmJIScnB71ej9vtlhID582bx8KFCzGbzcyZM4cTJ04gl8sZM2YMCxYsQKlUXnLNvr+/n4qKCtavX49eryc7Oxuj0YhSqaS2tpYTJ04MuK6R3JXMzEyMRiM+n4+tW7fyxhtvoFKpKCoqYty4cURHRxMdHc2kSZMoKioiISEBmUxGR0cHLpeLhIQECgsLCQQCbN++nQ8//BCv1ztsg4HItTl06BAqlYqSkpIrmp0RwYAgCANEGsh897vf5YUXXuDIkSOUlJQMux7xGo0GvV5Pc3MzTU1N+Hw+FAoFarUapVIpDWzhcFjKfFer1QMG0ci1UKlUBINBnE7noOfr7e0lGAwSFRV1wTq9QqFAqVSiVCrp6+ujr68Pn8+HXC6/IJdBLpdLx3C5XOh0OpKSksjPz2fnzp0kJSXR2dlJTU0NJpOJ4uJi2tvbqa6uxuVysXz5cmw2G93d3QQCARQKBdHR0Vit1gHbGr8och02bdpEVVUV999/PwsXLiQjI4OOjg6eeOIJtm3bhs/nY9KkSeTm5nL//ffz6aefolKpmDFjBn/4wx8u+ZlEgqZNmzaxd+9eHn30UW688Uby8vKQy+Vs3LiRzz77bMD1V6vVTJ8+ndzcXPLy8vB6vdTU1PDf//6XKVOmMG/ePMrLy3nttdcIBAIsWrSIH//4x9IxVCoV+fn5FBUVMXv2bMLhMGfPnqWiogKlUsnPfvYzqWHScBFJmKysrKS5uZni4uKv1KL6UkQwIAjCBRQKBbfffjsbNmxg//79JCcn88ADD0hNYoYDq9XKkiVLmD9/vlRa2W63s3//fv74xz/yyCOP8MILLzB+/Hhp18Rgd9ORLoB+v3/Q8/n9finZcrDjKBQKvF4vfr+fYDA4oOPh+SJBiM/nA2D8+PE8+eST/Pa3v+Wee+5Bq9Uyfvx4fvnLXzJx4kSeeeYZFAoFJSUlmEwmVq1axfvvv093dzexsbEUFhbyzDPPDNiC+EXBYJDu7m42btzIwoULKS8vp7CwEIDY2Fgee+wxHnzwQY4ePcqWLVvIzc0d/OIPIhAI0N7ezhtvvMH06dO59dZbycvLAz5fEoiNjR1wPeRyOWVlZRdc55kzZ1JVVYXD4aC/vx+NRjPoOUtKSigpKRnwvalTp3Ls2DEcDgc9PT3DcveBz+fj+eefJyYmhhtuuOGKd42IYEAQhIuKi4vj4Ycf5l//+hf//ve/KSkpGVa5A5Fp47S0NCmASU5OZv78+SQlJVFWVsZ7771Hf3+/lGg3WOvdQCCAXC6/5FKJRqO5oHXw+cLhMH6/X9p2F9kKeLHtm5Etgmq1Wqr7UFJSwrZt2wa0Rw6FQuzdu5eKigruvfde8vLyWLduHevWrWP9+vVkZmZKa/v33XcfmzZtGjSpsKenh5MnT+J0OpkwYQJxcXEDfl5QUEBCQgLHjh2jpqZm0OtwKQ6Hg6amJjweD6WlpcTGxl7WcSIzHJFWxl9XVFTUgHbLw02kxsS+ffv429/+xoIFC674mCIYEAThApHBc968eTQ2NrJ582Yef/xxNmzYgMFgGBZliiPv4YszGWq1mrFjxxIVFUVbWxsul0vaLuh0OgdUXQyHw3i9XrxeLyqVatAELZlMhtFolO7mv5jR7vf78Xg8UpU4k8mEVqu96NJDMBiUnmswGKT+9F+ccQgGgzgcDlauXMldd93FlClTpOnvoqIi8vPzSUxMxO/3U15eznPPPUdvby+xsbEXnR3w+XzScolOpxvwnMj5dTodCoUCl8v1ZZf/opxOJ11dXcDn2z6/bI07FApRUVHBe++9R11dHV1dXbjdbmw2m3ScL1NVVcXOnTs5ePAgNpsNl8tFW1sbHR0dF8wYDAcdHR3s2bOHNWvWcMcdd1BcXHxF9QUiRDAgCMKg4uPjKS0tpa+vj40bN7JhwwaWLl2KyWQaFgHBxchkMmlaORgMolarMRqN0jLCF5cCOjs78fl8xMTESJn8F2M2m9Fqtfh8vgu2Y7pcLqlufGpqKomJiej1euDzqoXn8/v9dHV1EQqFSEpKuiDBMKKjo4Ndu3YRHx9PYWEhJpOJU6dO0dbWRk5ODjExMajVamJjY0lLS5MK0/j9/osOwpFcBfh8JuRipagjMyRfTJD8Os6f2biUSDXGl19+GYfDgdlsZuzYscjlcvbu3TtoMuf55+np6WHjxo2cOXMGlUrFtGnTUCqVHDx4kAMHDlz2e7hWent72bt3L5s3b0ar1bJkyZJvrKGYCAYEQRhUpPBOOBxmz549vPbaa2RnZzNlyhRMJtNl72n+NjgcDux2OxkZGQO+HwqF6O/vJxQKER0djcFgICEhgcTERGw2G16vd0ChpTNnzuD1eklNTb1krX2DwYDZbKarq2tA9ziZTIbdbsdmsyGXy8nNzcVsNmMymdDpdLS0tEgJYTKZDLfbLW1hzMjIuOhshNvtpqGhgW3btlFeXk5KSgoajUbKazh/cJDL5dLXPp9v0H4TarUag8GAXC6XtuBFhMNhXC4XbrebqKgozGbzl38AF6FSqaRAwuFwXLLCpd/vx2az8eabb7J48WIWLlzIrFmziImJ4R//+AeHDh265LlCoRBtbW385z//IT09nZtvvpmlS5diNBp59dVXB2xhHOoiS0xHjhyRKlyWl5cza9asb6zN+ND9nywIwpCg0WiYMGECq1atorW1lVWrVvHJJ5/g8XikQWwo2r17N0888YSUBxB5eDweDh8+TCgUIjMzk7S0NGJiYpg5cyY1NTVST4bIevLu3bvp7+9n9OjRTJkyRVrn9/l8+P1+QqGQlPA3efJkaYtcZDANhUI0NDRw+PBhlEqltFaemZlJRkYGhw4dkpIPQ6EQ3d3d7N69G4VCQXFx8QW9BcLhMA0NDVRXV9Pa2sqyZcukAVatVpOQkDDgPXg8Hrq6upDJZCQkJAya96DX6xk9ejR6vZ4TJ07Q09NDKBSSrltNTQ3d3d0YDAays7Mv6zMxGAxYrVYUCgUHDhygr69POscXcy28Xi+nT5/G7/dTVlZGUVERiYmJg94FR4K3yOsNhUKcPn0aj8fDhAkTmDt3LsnJyZfcUTEURd5PS0sLzz77LFu3biU/P58nnnjigpoYV0IEA4IgfCmtVsuECRN455136O3tZdWqVaxYsYK+vr5r/dIG5XK5aGho4O2336arqwuv10trayuffPIJDz30EEajkcWLFzNt2jSMRiO//vWv6enp4ZVXXuGjjz7CbrdTWVnJmjVryM/Pp6ysDKvVSigUYtWqVUyePJkHH3yQffv2See86667KCgo4OjRozz77LP09/dz+PBh3nrrLbZv386yZcvIzs5GpVJRVlbGbbfdRktLC8888wwtLS3U19ezdetW1q5dyw9/+MOLJvK5XC5p+99f/vKXAbMzSUlJzJ49m23btlFdXU1jYyOHDh3inXfeYf78+ZfM95DL5ZhMJh566CG2b9/OW2+9xWeffUZ3dzdHjx7lN7/5DW1tbRQXF3Pbbbdd1mei1Wqlu/T333+fDRs2UFlZSVdXF0eOHOHVV1+lo6MD+HwWITU1FZlMRlVVFfX19fT29lJfX09VVdWAvIxIdUa73c7Zs2el3ACLxYJCoeDUqVMcPHgQt9tNfX09Bw4ckM4z1Pl8Pmpra7npppuoq6tj6dKlrF69Wlpm+qbIwkM1rBcEYUgJh8O43W527tzJpk2bOHjwIPn5+fzxj38kLS3tqtfA/7p27tzJK6+8wunTpwkEAgSDQRQKBVqtFovFwl133cXkyZOl4jQej4c333yTLVu20NLSgtfrRalUkpmZyZ133snEiRMxmUwEg0GefvppNmzYwKxZs7jnnnukPd5er5eDBw+yY8cOtm7dSn9/P1FRUZhMJiZOnMiPfvQjsrKykMlk+P1+Wlpa+Oijj3j77bfp7++XihBlZGTw6KOPMmbMmAumgdesWYPdbicrK4vbbrttwN2h2+3m7NmzrFy5kqamJik/wGQy8Ytf/IKioqJLTisHAgHOnTvHiy++yNGjR+nq6sLv96NQKIiPj+emm25i5syZ5OXl0dnZye9//3s+/vhjenp6MJvNzJgxQ0pqi4+Pv+g5PB4PJ0+e5Omnn6axsVEq9pSSkoLFYmHjxo1otVpKS0t55JFHePHFFzl06BAymYzExERyc3NJTExk9erVGAwGFi5cyK9+9SuOHDnCihUrpEqSY8eOZcWKFbzwwgtUVVXR3d0tfQ4mk4n33nuPM2fOMHv2bFavXo3ZbB5yeTA2m43Kykr+/Oc/4/P5ePjhh5k5c+Ylt4heLpEzIAjCVxLJJi8qKsLtdhMTE0NlZSWrV69m8eLFjBs37rLXkq+GjIwMlixZwunTp6UytgqFAr1eT3p6OlOnTh1QcVCr1TJ37lz0ej0tLS04nU40Gg3jx4+noKBAGtxkMhlz5szBaDRitVql0sHw/zsVdDodRqORc+fOoVarSUlJISsra0DhJqVSSVJSEuXl5SiVSjo7O5HJZNI0fGZm5kUHp/HjxxMVFUVycvIFU/4ajYa0tDSWL19ObW0tHo8HrVZLSkoKEyZM+NIBRKFQkJKSIn2eHR0duN1ulEolVquVyZMnk5KSgkKhQKfTMXPmTLKzs6XKiyaTiVGjRl0y4FCpVOTk5HD33XdLn41CocBkMpGenk5qaqpUMjg5OZnly5czefJkXC4XarWa1NRU0tLS0Gg0BAIB0tPTiY6OZsKECTzwwAN0dnYil8uxWCwkJCTw/e9/n/z8fDo6OpDJZIwePZrU1FRGjRpFW1sbCQkJ0o6NoSCyxHHgwAE+++wzdu/eTSgU4u6772bWrFmMGjXqGw8EQMwMCIJwGfr6+qipqeGll16ioqKC733ve8yfP5/CwkIsFgtyuXzI/HIVhOEiUl/i1KlTbNq0iYqKCjo7O1m8eDGPPfYY0dHRVy1pVwQDgiBclsiywV//+lf+/ve/Y7Vauemmm3jwwQeJjY0VAYEgfEWRJEGfz0dzczP33XcftbW1FBQUcMcdd7B8+fKr/hpEMCAIwmWJ/OoIhUJs376dtWvXsmvXLsxmMytXrmTOnDlXtB9dEEaKYDDIkSNHePfdd1mzZg0Wi4UVK1ZQWlpKUlLSt5LLIIIBQRCuWF9fHw0NDezdu5e3336buro6ysrKWLRoEVOnTh1SuQSCMFSEw2Gqq6ulxk2dnZ0UFxdz7733kpWVRWxs7LeWmCuCAUEQvhEej4fW1lb27NnDli1bOHPmDCkpKYwfP56SkhKmT5/+je6LFoThyufz0dXVxa5du/j4449pbm5Gr9czbtw45syZw4wZM1CpVN9qUS+xm0AQhG+ERqNhzJgxWK1WsrOzWbt2LTU1NdTX13P27FmUSiWpqakYjUapxr0gjBSRnIDu7m5aW1s5fvw4GzdupL6+nilTprBgwQLKy8tJTk6+Jq9PzAwIgnBVBAIBqcnM+vXrCQaDLFu2jEWLFjFp0qQB7WrFbIFwvYokBwaDQanp15YtW9i3bx8pKSk89dRTlJaWXvOlNBEMCIJwVUR+CXq9Xtra2nj55Zd5/fXX6evrY/To0dxyyy3ce++9w7rpkSB8me7ubo4fP84///lPNm/ejF6vp7i4mFtuuYVbb71Van19rQNiEQwIgnBVhUIhAoEAHR0dNDc3s2fPHvbu3cvhw4eJjo5m4cKFzJ49m3HjxpGUlHStX64gXJHIkFpdXc2nn35KZWUlx44dQ6vV8p3vfIcZM2YwZswYEhISiI+Pl9pDX2siGBAE4Vvj9Xo5c+YMJ06c4PDhw+zbtw+Px4PZbMZqtZKTk8PEiRPJzc1Fq9UO6a6IgnA+t9tNe3s7dXV11NTUcPjwYdrb25HJZJhMJoqLi5k6dSqZmZno9foh929bBAOCIHzrgsEgLpeLjz76iM2bN1NXV4fX68VsNlNSUsLs2bNJSkoiPj6euLg4oqOjr/VLFoQBImWD+/r66OzsxGazcfLkSfbt20dVVRV+v5/s7GyKioooKyujuLgYGLr5MSIYEAThmgqFQhw/fpzKyko+/PBDPv74Y6KioiguLqa0tJT58+dTUFCAXC6X1laHytSqMHJEhspIEBAKhXA6nezYsYNNmzaxZ88e7HY7ZrOZW265hWXLlpGTk0NsbOw1fuVfjQgGBEG4piKJhpGMa4fDwfvvv88HH3zA/v37sdlsWK1WFixYQGlpKYWFhWRnZ4tgQPhWRYKA/fv3Sw2Edu/eTXt7O+PHj6e8vJyysjKmT58u9RAYTkGrCAYEQRgyIgFBT08PDocDm83G8ePHpYRDp9OJTqcjLS2N6dOnM2vWLLKzszEYDENuDVYY/jweD3V1dRw9epSDBw9KjYNiY2NJTk4mOzubkpISMjMziY+PJzY2lpiYmGH5b1EEA4IgDFkul4v29nZOnTrFqVOnaGxspK2tDbvdTjgcJiEhgbS0NFJSUhg1ahRWq5XRo0ej1+u/tTKuwvUhHA7T29srrf+fOXOGM2fO0NzcjN1ux+v1olAoSE9Px2q1kpaWJiW9Xg/BqAgGBEEYNmw2G3V1ddJd2okTJwgGg8TFxZGWlkZWVhaTJk0iJSVFSjzU6/XExMQQFRU1bKZshasvEAjg9XpxOp309fXR29uLzWajsbFxQPCpUCgwmUyMHTuWGTNmMGPGDMxmM1qt9lq/hW+UCAYEQRiWwuEw9fX1VFZWcvDgQaqrq6mqqsLlcpGUlCQFBpG+CAaDAaVSedEkRBEkXL++mPgXyU/p7u6msbGR/fv3s2vXLvbs2UNnZyehUAij0SgtQ02dOpWsrCzi4uKu8Tu5ukQwIAjCsHX+L/hQKITH42Hv3r0cOHBA2utdW1uLXC4nPT2dzMxMcnNzKSgoYPLkyYwePVpK9hKuTz6fj+bmZurr6zl27JgUOJ49exan04lGo+GGG26gpKSESZMmkZeXR0ZGhjSTNFJ2r4hgQBCE60Lkjs/pdOJ0OnG73fT399Pe3k51dTVNTU2cO3eOc+fO0draikKhIC4ujqSkJNLT08nJyWHs2LFYrVZMJtN1fyd4PYkknra2ttLS0sLZs2c5ffo0x44do76+nt7eXvx+P1FRUaSnp5Oamiqt+WdlZREfH49er0en06HRaFCr1cDImjESwYAgCNetyGxBU1MT7e3tdHV10dnZKQUFPT09+Hw+gsEgoVAIlUqFwWDAYDAQFxdHfHw8JpNJKn5kMBhISEjAYDCMqIFiqAgGgzidTjo6OrDb7fT09NDT04Pdbsdms9HZ2YnD4cDpdOLz+QiHwyiVSgwGAyaTCYvFQkpKCklJSZjNZsxmMyaTSeSTIIIBQRBGmMhdZHNzM42NjTQ2NnL69Glqa2tpaGjA7XYTDAZRKpXEx8djsVgwGo2YTCbMZjOpqalYrVbUajUqlQqlUolSqRzwdVRUlLSbYaQPMl9FZFbH5/Ph8/nw+/34/X7p68jD7XbT1dXFmTNnOHfuHN3d3XR3d9PZ2UlzczNyuRy1Wo1eryclJUWa7cnIyCAtLQ2LxTIipvwvhwgGBEEQ+HxA6uvro6GhgaamJimr/MSJE9TV1dHV1YXL5ZK2NFqtViwWC4mJiaSkpGC1WklJScFisUh3nQqF4qKJil/253B3/rByfgLfF/+M/D0UCuH1emlubsZms3Hu3Dna29tpbW2lqakJm81Ga2srnZ2duN1uZDIZycnJUmCWkZFBXl4emZmZ0jKPSqX6lt/18CaCAUEQBAYfrM7PRejo6KCuro7a2lrOnj0rDVo2m42mpia8Xi/hcBi5XI5KpSI1NZVRo0ZhNBoxGAwYjUYsFovUsS7yMJvNxMfHExUVdS0vwTcm0nvC4XBgt9tpa2uju7sbh8Mh3clHlmva29vp6Oigo6NDSgZVq9XExsYOCLKSk5MZNWoUGRkZjB07ltjYWCnYAsQOkSskggFBEISvIBgMSnvT3W63NJ0dCASkaWy73Y7dbpdyE7q6unA4HLhcLlwuF/39/fT19eF0OgmFQsjlcqKiooiKikKtVhMdHS09tFqt9NBoNGi1WnQ6nfS1Wq1Gq9USExMjDXyRIOT8gktKpfKie+IjOzAisx2R73m9Xvx+P/D/7acjyZgejwePxyNdA5fLhdvtxu124/F4cLvd0p59r9dLIBAgEAjg9/ulKXydTifVftDr9dIjISGB5ORkzGYzsbGxREdHo1QqUalU0ntSqVSo1Wo0Gs2AQEC4ciIYEARB+Ia4XC6cTqc06DscDvr6+qTBMvLzyAAaeTidTjweD6FQSDrWpX41D/azSHChUCik7ykUikGrMYZCIXw+34DjBgIBgsHgoOf44gAc+TocDg+4K48EIZGA5YtBjU6nGxD86PV6DAYDer0ejUYjKkh+y0QwIAiCcA1EAoPe3l4pES4yaxCZSYgEEZFA4vy78MjD6XRKxzy/7kJEpMPexchksgGBA3BBUSa5XI5OpyMmJgaNRjNgViIyqEe+r9Fo0Ov1xMXFSXf8cXFxGI1GtFrtBecShg4RDAiCIFwn/H4/Ho+HQCAA/P+dv9vtvuC5kbv4uLi4AXf0kWl4MQU/sohgQBAE4TrxTf46F8HAyHJ9pK4KgiAIYgAXLpsoyC0IgiAII5wIBgRBEARhhBPBgCAIgiCMcCIYEARBEIQRTgQDgiAIgjDCiWBAEARBEEY4EQwIgiAIwggnggFBEARBGOFEMCAIgiAII5wIBgRBEARhhBPBgCAIgiCMcCIYEARBEIQRTgQDgiAIgjDC/R9Q8OZgA8CR0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([1, 2], N / 2),\n",
    "    'y': np.repeat([0, 1], N / 2)\n",
    "\n",
    "})\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMClassifier, lgbm_params)\n",
    "display(predict_and_evaluate_classifier(df, [\"X\"], \"y\", model))\n",
    "display(predict_missing_value(model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6339c0fe-f6b2-4961-ae6a-9aa2b98eded1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>y_pred_proba</th>\n",
       "      <th>count</th>\n",
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       "  <tbody>\n",
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       "      <td>500</td>\n",
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       "      <td>500</td>\n",
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      ],
      "text/plain": [
       "   X  y  y_pred  y_pred_proba  count\n",
       "0  1  1     1.0      0.634166    500\n",
       "1  2  0     0.0      0.365834    500"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "    X  y_pred\n",
       "0 NaN       1"
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     "metadata": {},
     "output_type": "display_data"
    },
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       "      <th>node_depth</th>\n",
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       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0-S0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>0.2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>-0.2</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_depth node_index  split_gain  threshold decision_type  \\\n",
       "0           1       0-S0      1000.0        1.5            <=   \n",
       "1           2       0-L0         NaN        NaN          None   \n",
       "2           2       0-L1         NaN        NaN          None   \n",
       "\n",
       "  missing_direction  value  count  \n",
       "0              left    0.0   1000  \n",
       "1              None    0.2    500  \n",
       "2              None   -0.2    500  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(n_estimators=3, verbose=-1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([1, 2], N / 2),\n",
    "    'y': np.repeat([1, 0], N / 2)\n",
    "\n",
    "})\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMClassifier, lgbm_params)\n",
    "display(predict_and_evaluate_classifier(df, [\"X\"], \"y\", model))\n",
    "display(predict_missing_value(model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02e5d029-0c49-4d07-9bd5-93a999ce5ed3",
   "metadata": {},
   "source": [
    "# Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf8b0af-f553-416f-8c49-5907aad24572",
   "metadata": {},
   "source": [
    "## NaN on training dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "243f019d-d342-42cf-a9d0-3b4c1e86ae50",
   "metadata": {},
   "source": [
    "### NaN reduce loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "592256a9-a92d-4e66-8396-554434dbb29f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSe is: 2.1687292025407732e-05\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000027</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.999973</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X  y    y_pred  count\n",
       "0  1.0  1  1.000027    333\n",
       "1  3.0  3  2.999973    333\n",
       "2  NaN  2  2.000000    333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_depth</th>\n",
       "      <th>node_index</th>\n",
       "      <th>split_gain</th>\n",
       "      <th>threshold</th>\n",
       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0-S0</td>\n",
       "      <td>499.5</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>2.00</td>\n",
       "      <td>999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0-S1</td>\n",
       "      <td>166.5</td>\n",
       "      <td>1.000000e-35</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>1.95</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.00</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.90</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>2.10</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_depth node_index  split_gain     threshold decision_type  \\\n",
       "0           1       0-S0       499.5  2.000000e+00            <=   \n",
       "1           2       0-S1       166.5  1.000000e-35            <=   \n",
       "2           3       0-L0         NaN           NaN          None   \n",
       "3           3       0-L2         NaN           NaN          None   \n",
       "4           2       0-L1         NaN           NaN          None   \n",
       "\n",
       "  missing_direction  value  count  \n",
       "0              left   2.00    999  \n",
       "1              left   1.95    666  \n",
       "2              None   2.00    333  \n",
       "3              None   1.90    333  \n",
       "4              None   2.10    333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(verbose=-1)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# def train_and_evaluate(df, lgbm_params, n_trees_to_plot=1):\n",
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([1, np.NaN, 3], N / 3),\n",
    "    'y': np.repeat([1, 2, 3], N / 3)\n",
    "\n",
    "})\n",
    "\n",
    "lgbm_params = {\"verbose\": -1}\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMRegressor, lgbm_params)\n",
    "display(predict_and_evaluate_regressor(df, [\"X\"], \"y\", model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5646c87a-f339-496c-804c-29d59d6e15d0",
   "metadata": {},
   "source": [
    "### NaN values with low loss reduction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d7e0266-762d-4100-9c29-c8d2feedcdeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSe is: 3.1819805155681027\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2.000060</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000007</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5.499967</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>5.499967</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X   y    y_pred  count\n",
       "0  2.0   2  2.000060    250\n",
       "1  4.0   4  4.000007    250\n",
       "2  NaN   1  5.499967    250\n",
       "3  NaN  10  5.499967    250"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_depth</th>\n",
       "      <th>node_index</th>\n",
       "      <th>split_gain</th>\n",
       "      <th>threshold</th>\n",
       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0-S0</td>\n",
       "      <td>1687.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>right</td>\n",
       "      <td>4.250</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.025</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0-S1</td>\n",
       "      <td>375.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>4.325</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.375</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0-L2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>4.225</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_depth node_index  split_gain  threshold decision_type  \\\n",
       "0           1       0-S0      1687.5        3.0            <=   \n",
       "1           2       0-L0         NaN        NaN          None   \n",
       "2           2       0-S1       375.0        3.0            <=   \n",
       "3           3       0-L1         NaN        NaN          None   \n",
       "4           3       0-L2         NaN        NaN          None   \n",
       "\n",
       "  missing_direction  value  count  \n",
       "0             right  4.250   1000  \n",
       "1              None  4.025    250  \n",
       "2              left  4.325    750  \n",
       "3              None  4.375    500  \n",
       "4              None  4.225    250  "
      ]
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    {
     "data": {
      "text/plain": [
       "<Axes: >"
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     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(verbose=-1)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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FhYW89NJL2Nvb06tXL1JTU0lNTcXR0ZGzZ8+SkJDA888/j16vJykpSTr/u/3/KBQKHB0dGT9+PBMmTGDNmjU8/fTTd/WY7mcEQRyoy8vFGXxpqTirT0uD06fhzBlx4E9JEddZBvPqAsONolbXHHgtikClsup9dUEEqkwJglAlVBiNVZqEv8Ny21r2adE8+PuLwkFkJNSrJy7u7qKWwdFR1DTY2Px7BARZGJC5Jzly5Ag7d+5k2bJlODk53e3DASAhIYGff/6ZnJwcNm/ezKhRoxg5ciTr168nMTERHx8f6tatS1RUFOHh4XzyySc0adKE7OxscnJy2LdvH2VlZeTl5bFr1y7Cw8Np1qyZZNtWKBQEBwczbNgw1q1bx8mTJ0lMTOSRRx4hOjqa/Px8ZsyYQUhICPPmzbti8B05ciQjR46UPr/22mu8/fbbjB8/nq5du/L6669TXl5OSUkJdnZ2TJkyhTfeeIMlS5Zw9uxZhgwZQo8ePYiLi+Pbb78FYOnSpVJ7nTp1YtKkSWzduhW9Xk9JSQk7duygT58+98ygq1AoCAwMZOzYsbzxxhv07t2bwMDAu31Y9w2CAJcuiQN8SgokJooDfnq6+Dk9XVTpXy8KhThoOjmJA6jl1dZWXDSaqsWi3re2rprdW0wAarU4QIM42Ov1VUKBTid+bzJVmR+02ppLZaUo1JSUVAk1paWilsIiVFQXXoxGSEgQl19/Fb9TqUQtQmCgqEUIDa1aLNoEG5ubv/Z3GzkDocw9h8lk4sUXX8Ta2pp58+bdM34COTk5nD59Gp1Oh7OzM02bNsVgMHDmzBkKCgqwtrYmJCSE4OBgLly4QFpaGg4ODnh6epKYmEhISAjl5eXk5ORgY2NDSEgIYWFhqFSqGvupqKjg7NmzXLx4EWdnZ+rVq4ezszM6nY64uDhsbW2vKyPh+fPnSUhIIDIykoiICI4ePYpOp6NBgwY4Ojqi0+m4cOECGRkZaDQaoqKi8PHxITc3l0OHDl3Rnru7O3Xq1OH06dOSWcHR0ZGwsDD8/Pzumf8JQKvVMmzYMNq1a8dzzz1317UW9xrVe32dDo4dg8OH4ehRUQDIyxOFgoKC61PfOzuLg6RloAwMBC8v8PAQhQHLYG9ZrKyqVPVWVuJicQT8p1h8DQyGKvOEXi+ep8UvwSIwFBZCbi5kZoqCTmqquFy6dH2aBXt78PQUz9XPT9QetGghLl5eVdvdD7efLAzI3HMkJCQwZswYFi5cKMeNy9w033//PV999RWrV6++Z7RLdxOLt355uTgIHj4M27bBb7+Jg79l4Lx88Le2Fme8NjbioO7jAzEx0KgR1Kkjzow9Pats9BY7fXXV/r34CFf3fbBELlh8GMrLRaHg/HnRHHLqlPi+okIUKizL5dfKIuTY20NsLPToAZ06iVoDBwdR83EvXguQzQQy9yApKSk4OzvXaheXkbleWrduzYIFC0hJSSE2NvZuH85dw2SCnBxxUDt8GH7/HQ4cEL37a0OjEWe13t4QHAxRUeKgX6eOaEN3d68a5O9nqvslXB6F6uoKAQHQpo34WRBETUJysmg6OH8ezp4VtQk5OZCdDfn5VRqJ8nLYvl1crK3Fa9iuHbRvL17HiAhROLiXujdZGJC558jPz8fV1VWOE5f5R9ja2uLi4kJeXt7dPpQ7jiCIdvHdu2HvXjh5UpzdZmdfOZtVKEQV9wMPiLPZunXB11f8LihIHLT+6ygUonnD4kgIohYhPx+yskSnyoSEKoHr3LkqXwa9Xrz2p07BsmWiQ2JMjCgYdOwoCliXWQrvCnJvK3PPodfrsba2vis2aKPRiE6nw2w2S2F4lkgGg8GAVqutsb2VlZUUs18dQRAwGAySZ7+1tbVUROnydTY2NpLgYzQa0ev1CIKAjY2NlIkQRF8Ko9GIlZXVTV0bSy4BvV6P2WzG1tb2Cn+F6vuyhDfa2dndU/4A14tCocDGxgadpVf+l2M2i7PSnBxYvx5WrBBnriUlVfH6IM6CbWzEgb5jR3jwQXFwcnKqsu/fSzPWexW1WtSeeHuLJhOjUTQjlJSIPgcHD8KWLbBnj6gpMBjE9SdOiMumTaJPRbNm8OST0Lbt3U2aJAsDMlclPz+f8+fPo9fr8fb2Jjw8nNTUVLKysvD29iY0NJTU1FQuXryIWq0mLCwMLy8vKioquHDhAsXFxVhZWVGvXj2cnJyuW+Vva2srxb/fab799lu++eYbDAYDgiDg7u7Oyy+/TMOGDVm/fj1PPvmkVLFQpVLx5JNP8vHHH9doQxAELl26xKJFizhy5IiU33/ChAl4enqSmZnJokWLOHXqFADt27dn7NixODo6smjRIrZv344gCLRr144XX3xRCl88dOgQv/32G+PGjcPd3f2Gz81gMPDDDz/w3XffcfHiRd5//32aNm1a67bx8fG88cYb7N69m7179xIeHn7D+7vbmM1mysvLsbe3v9uHclsxGkUB4ORJWLUKfvlF9Amo7g1ma1sVKte5M/TqJaqrqw88sgBw81jCEZ2dxSUgQNS0PPWU6I/xxx/w00+io2Zmpvj/lJaKS3KyKLw1awaDB1eZEu5Q/TUJWRiQuSoFBQXMmzePP//8k8WLFxMUFMSZM2d4//33GTlyJBkZGXzwwQeEhYVRXFyMRqNhzpw5/PTTT/z+++8EBASQmJjIhAkTaNmy5XXv18vLi9zcXGmGfCf9BjIyMpg0aRJt27aloKCACRMmMGvWLNasWYMgCDzyyCNSdj+FQkGTJk1qbeebb74hLi6OTz/9FKPRyLPPPsuaNWsYN24cS5cuJScnhy+//JLCwkKef/55fH19adGiBWvXrmXJkiU4OTnx+OOP06FDB9q1a0dpaSm//vorXbt2xc3N7abOraCggDVr1vDkk08SGBiIi4vLVbeNiYlh8uTJHD9+/Jptms1m9uzZQ8OGDaUsifcKhYWFlJWVSQmT/o2kp4szzJ9/Fk0C1as5K5Wizd/i3d6ypagBkNX+tx9Ll6VSiZqD/v2hb18xUuPQIdGUsG8fHD8u+iIYjeLn/ftFM82jj0Lv3tC4saipuRPcsDCg04k2EJn7F4t38N8RGRnJhAkTOHnyJL6+vtja2tKiRQvq1atH3759GT16ND4+Prz55psUFhbSrVs3tmzZwpYtW2jXrh1jx44lNze3RjGe6yEiIgKTyUR8fDzt2rW7ybO8OQYNGoS3tzfW1tZSOd/169dTVlYGQN++fenXr9812zCbzaxZs4aBAwcSGBiIyWSie/fu7Nq1i0cffZT169fz7LPP4uvri7u7O23atGHHjh1oNBqsra2JjIzE0dERNzc3kpOTadu2LZs2bcLLy4tmzZpdUziqLe2xZfuKigqysrJwcnIiJibmCtW/5XfVX2t7X73N9PR0PvvsM9544w0cHR0lM8jl291pR1BBEPjtt9/w9fUlKCjoju77dmK5tHl5sHatqAk4c0ZUTVtQqaBBAxg2TPRkDwoS0+/eh5aefxVKpegvEBkpDvaZmaIwsH49/PijKMgJAsTHi/4H330nmnEmTBB9OeD2am9uWBhYtAjeflsWCO5npkyBWbOub9uOHTsSFBTEhg0baNWqFZs3b6ZTp06UlZWRmJjIo48+SlZWFoIgoNFoSElJISoqio8++ojS0lJ69eqFh4fHDR2fr68vbdq0YenSpbRq1aqG3fx2U33gsKRE9vHxkWbRly5d4vz58yiVSmxtbfH09MTakhnlL7Kzs8nNzSU4OFgqAhQVFcWyZcs4ffo0hYWF+Pn5oVAosLa2Jjw8nM2bN9O/f3+MRiNFRUUolUrKyspwcXHh+PHj7Nu3j/fff/+KfV1OZmYm8+bN48SJE5hMJho1asSkSZMwGo1MmzaNU6dOMXbsWMLDw/nxxx9rDNKVlZWsWrWKtWvXUvFXZhlLQaOUlBRmzZpFcnIyRqORhx56iHHjxvHGG2/w448/cvr0aR577DFGjBjBa6+9RlJSEkajkR49evDSSy9he4d1nmVlZaxevZqRI0fW6tNxP2JxCty+HWbPFoUAiy+AWi16+XfqBKNHQ/PmYnibXLTn3sTOTowoCA+HPn1EwWDpUlHLk5QkCgYJCeL7DRtgzBgYNUpMbnSr8jFczg0LA3p9VeYmmfuTG/GnUqlUjBo1ik8//ZRTp07x559/Mn/+fEpLSzGZTOzcuZP09HRADOWKjY2lXbt2BAYGsnnzZr777jumTp3KwIEDr3t2qFAoGDZsGIMHD+b333+nS5cudyXEMDU1lVOnTvHiiy+iVqvx8/Nj165dHDt2DKPRyMWLF+nXrx+DBw+uMeAUFhZKwpHlfDQaDVqtlpycHJRKZY11tra2lJWVERERQXR0NEuWLMHKyorw8HBCQkJYvXo1Y8eOZd++fWRmZuLh4UH79u2xs7Orcbwmk4lXX30VrVbLypUrUSgUTJs2jXnz5vHuu++ycOFCBg4cyMKFC2ljiZn6C0EQ2Lx5Mx988AHvvfceHTt25MCBA4wZMwYQHSWHDh1K69at2bZtG1OnTuXJJ59kxIgRnDp1irVr1xIaGkpmZiZDhgyhdevW7NixgylTpvDkk08SEBBwO/+qGhiNRtasWYNCoeCRRx75V4SnVlaKKuSVK0VtgOUZVipF+3LHjqIauksXWQNwv2AJbdRoRKHgzTfh6afh++9FbcGxY+JYm58P77wjmoImTIBHHhFzPdxq/pHPgEpVlSJS5t7GYKjK4X2jdOzYkY8++ohPP/1Uyl6nVqtxcHCgV69eTJ06VdpWp9NRVFTEE088waOPPsrcuXPZtGkTffv2vWJWa1En19ZZh4eHM2LECBYtWkRkZCTBwcE3d/A3SVFREStWrKB37948+OCDADRv3pzY2FgcHByoqKhg9erVzJ8/nwYNGlzVEa86l6vhL8fOzo5Zs2ZJRX+GDx/OgQMHiImJITk5mZUrV9K5c2fWr19PRkaGNFBbuHTpEnv37mXmzJkEBQVJbUyfPp3MzMxrOtLp9XqOHj1KnTp1aN++Pba2ttja2kqmhICAAGlADwwMxMbGhtLS0iva8ff3x9/fX/qNRqOpdbvbhSAIxMXFsXr1aiZNmoSjo+Md2/ftoqwM5s+Hr78Wnc0soYF+fvDEE+LgEBMjagJuJWazmW+//ZYdO3bQtm1bBg8e/Leaqau1c/z4cb777jvMZjPPPfecdI9YEASBgoICfvjhBzp37kxoaOitOo37BoVCdDycOFF08Pz9d1i+XAwNNZvFehBTpohJop5/XtT+3MqQxH8kDAwZAi+/fOtvQplbzxtvwFdf3fjvFAoFXl5edOnShRUrVvD8888Dosd/7969+e6772jUqBENGzaUVMsrVqygTZs2hIeHYzKZCA4OrjU0bc+ePbz77rtERkbStGlTGjdujLe3N1ZWVlhZWTF48GAOHz7Mp59+yrRp024oIuGfUFpayooVK/Dw8GDYsGFSB6jRaKQZvZOTE48++iizZs0iKSmphjDg5uaGQqGQrocgCFRUVGBvb4+/vz+CIEgRCRaPd0dHR8lPITQ0FEEQOHXqFBcuXGDSpElMnjyZpk2bMnbsWOzt7Vm5ciXDhw/HpprzR35+PkajUYo0UCgUuLm5odfr0Wq11xQGtFotubm5BAYGXhFuaBlgP/74Y06fPk1eXh7JyclXCDWCIBAfH88nn3zCyZMnycvLIykp6W9LN98qLGadBQsW0KJFC7p06XJH9nu7MJlENfHLL4shan/dMjg6ipntXnoJ6te/faGACoWCjh07smfPHg4cOMDAgQNvqp2MjAwWLVpEnz59uHTpEgUFBTWEAbPZzLlz55g6dSrx8fE0aNDgpoSBsrIypk2bxu+//866deuoW7dujfUHDhzgnXfeISsrC4CwsDCeeuop2rdvz8qVK5k9ezaGaipvhUJB586deeSRR3j99dfJz8+X1j3wwAOsX7/+ho/xelAqRRNCSAg8/LAoEHzwgRgxUlYmag4OHBD79IEDb52D4T8SBhwd5aQU9wv/ZIKk0Who27Yter1esnUDTJkyBScnJ7788kvMZjMtW7Zk4MCBBAcH89VXX1FRUUGjRo0YN25crfHsFnv4zz//LA0Y/v7+NGnShNjYWGJiYmjbti1LlizB0dGRZ5999ramlRUEgezsbL799lvc3NwYPHgwarVaOt/z588TFhYm5QSorKxErVZfYZP28fHBx8eHlJQUKbY/Pj6e8PBw6tWrh7u7OxkZGQiCgF6v58KFC9StW7fGYF1cXMyKFSsYOnQoHh4eFBQU4OLigpWVlRS+qdVqawgDlkRNhYWF0vkUFxdjb2//t+F1KpUKW1tbiouLrxi8y8rK+OijjzAYDKxevZrU1FRGjx59RRvl5eUsWrRI0pqkpaXx5JNP3sA/8M8oKChg7ty5GI1GnnnmGezt7e9bE4HBIKqFX3tNDBkUBNHpt1EjmDxZdEC7XbZjCwqFAhcXF5ycnCi6WrrC6yArK4vMzEzq169PVFTUFesLCgrYtWsXjzzyCJmZmTe1D0EQ2LhxI19++SVWVlZSDo/qpKWlERoaypIlS/jjjz947rnn+OWXXzhx4gQlJSXY2toSHh6OQqHAZDJx5MgRmjRpIj3ndevWle6nkJCQmzrOG0GtFvMQvPgi9OwJCxeK/gN5eWKq5KeeEpMbTZwoRiz803tBDi2UuSqWxDsmkwmdTkfHjh1rRAZYW1szYcIEJkyYUON3TzzxBE888cTftu/n54eNjQ3mainRMjIyyMzMZOPGjZJWQq1WM3fuXPLy8hg7dmytHcqtQKvVsnjxYnbs2EGnTp2YOXMmIAotAwcO5LPPPuPxxx8nNjaW0tJSvvzySyIjI2nQoAEHDhzg4MGDDBo0CA8PDwYNGsT27dsZMGAARqORbdu28fDDD+Pt7U3fvn3ZvXs3vXv3pqCggH379vHEE09I19ZoNLJy5UoaNmxIo0aNpNLEZ86cIT8/n/379xMSEnJFlIa3tzdNmzZl69atdOzYEYVCwZo1a2jUqBG+vr6SkFAbtra2xMTE8OGHH3Ls2DFiYmKIi4tDq9ViNBqpqKggPDwcJycn8vLypORLarUag8FAUVERdnZ2lJeXExQUJG1n0YDcbjIyMpg/fz7p6enMmTPnvq5UaDaLfgGvvy5WCgQxIdDQoTBpkugjcLdkHEEQOHPmDFu3bqW0tJRGjRrRpUsX1Go1f/zxB/v378dgMFC/fn26d+9Ofn4+K1as4OzZs7z//vs0bNiQZ599tkabHh4ejB8/niNHjlwxacjKymLt2rV07dqVBg0aXPW4zpw5w/Lly4mMjCTFctEuo0GDBkRHR+Pp6UmDBg0IDg7m8OHD5OXloVQqefnllxk4cCAqlYpDhw4xefJkHnvsMXbv3s2AAQOYNGnSHc+KavErqF8f3n9fTI+8YIGYtEivFwWEvDyYOVPMI/FP7ov7QhgQBAGz2Vxj0FAqlSiVSul7hUIhfXejWNqwxLRb2qptVmEJsbJsb9mnJaTKsg6o0U7131iO37Kve5WjR49iMplQq9UcO3aMp59++qoZ667F1ULWVCrVFU5wl2+Xm5uLWq2mTZs27Nmzh6KiIt54440aGopbhVar5ezZsxw7doxjx45J3wcEBNCqVSuCg4N55ZVXMBgMKJVK/Pz8ePfddwkKCmLbtm2sXLmS7t274+HhweDBg7l06RJPPfUUCoWCNm3aMGjQIKysrBg5ciSLFi3iiSeeQKVS8eCDD9KnTx+po9m/fz+FhYU88cQT0jmOHj2aWbNmMXjwYMm34PL/QqVS8dprrzF37lyGDRuGIAiEhYUxadIkzGYzr7/+OklJSbz11ltMmjSJbt26Se2rVCoee+wxEhMTmTJlCp6engQGBmIwGFi1ahWtW7dm1apVnDp1igYNGmBnZ8fbb7/NtGnTiI6OZuLEiTz88MN06tSJL774gjNnztCgQQPs7e15++23Wbx48W3JQyAIAhcuXGDmzJkolUrefffd+9rebDbDmjXwyiuihzmIYYHz5okOgq6udzc64OzZs0yfPp3GjRsTGRnJkiVLMJlMdOzYkXPnzhESEoKNjQ3z58/H2tqaTp060aRJE/bu3Uvnzp2pZ8nle51cvHiRpUuXEhgYWKswIAgCJSUlfPTRR/Tv35/169dfVRioW7cugiBQXl7OwYMHSU5OlsyZPj4+2NjYoFQqqaysZMWKFbRr107KUWE2mzEajdIYcbNjzT/BwUEUCOvWFTVG27aJUQcrVoi5Cj766B9q6YUbZPZsQbCyEus9PfOMIJSW3mgLN05RUZHw6quvCmFhYYJGoxF8fX2FyZMnC7m5ucJLL70k+Pr6Cl26dBF++umn62rPbDYLRUVFQk5OjmA2m4UVK1YIHTp0EFq1aiU0bNhQ6NWrl7B9+3bBZDJd8dvS0lLhrbfeEpo3by488MADwvjx44WMjAxBEAShsLBQeOWVV4QWLVoIDzzwgDB58mQhOztbMJvNwsWLF4WpU6cKLVq0EBo3biy88sorQm5u7i29Ttdi0iRLjS5BmD79+n5z+PBhoW/fvsKQIUOE06dPC2az+Zrbm81mwWg0CuXl5UJeXp6Qnp4uXLhwQTh16pSwY8cO4csvvxRef/114amnnhI6d+4sNGjQQPD39xeAWhc3NzehQ4cOwvfffy+UlpYKqampwrhx44QOHToIe/bsEbRa7T+/MJcdv1arFSoqKmoslZWVgtFoFAwGg1BZWSl9r9PppGtiMBgErVYr3TNms1navrKyUjAYDNK2ZrNZ0Ov1ta4TBEHaV/XvzGazoNPphIqKCkGr1V71v6i+XUVFhaDX6wWz2Vzj3GrbZ23HbGlHp9PV+N5y7Jbzrb6d0Wisdbu/u3du5r8qKysTtmzZIrRs2VKYMmWK9KzdrxgMgrBxoyCEhorPqVIpCNHRgrBzpyAYjXfnmCorK4Xp06cL48ePF8rKyoQvv/xSaNeunZCRkSEYDAZh+vTpQr9+/aR7xGQyCUVFRcLAgQOF9957TzAYDMLu3buFNm3aCGfPnr3mvg4fPiw0adJEOHDggPSdyWSSnr/aMBgMwooVK4SXXnpJyMjIEB588EHBwcFBOHHiRK3bl5aWCn369BHq1KkjNGvWTFi/fn2NfsRsNgvbt28XmjdvLsTFxQlms1n46aefhJ49ewodO3YUmjRpIrRu3Vr47LPPhIqKipu4ov8cs1kQcnIEYdgwQbC2rrpXJk4UhLKym2/3vtAMODs789prrxETE8OYMWOwt7enf//+ODk50ahRI0pKSpg9e/bfpmgVBIHS0lL+/PNPzp49S3BwMI8++igpKSkMGjSIIUOGsGbNGmbMmMHEiRPZtWsXXtWLUiOWRZ03bx7Tpk0jKiqKJ598EhsbG+bMmcPKlStZvHgxc+bMwcXFhfHjx2Nvb8/06dP5/PPP+fLLL/nggw+oqKhgypQpuLi4MHHixJvy0L0TNGnShO+///6q6ysqKsjNzSU/P5+CggLy8vLIyckhKyuLixcvUlZWhl6vx2QyYWdnh4eHB76+vjRv3pzHHnsMX19fVq5cycKFCzH+VTxcoVBgb29Pq1at6N+/P/3795cy7jk4ODB37ly+/vprZs6cyUMPPUTfvn0JCQm5JVK6JZf9tbiamlCtVtdYZ6lrUNv2CoVCcpKsjdq0L5acBH/Htbb7u3O71jFDzXOvfuyX76/68d+OHBEWh7NVq1bxxx9/8Mwzz9C/f/87nsvgViIIYna6t94SIwYUCjHRzHvvidXu7oVCNkajkYyMDIqLi/n666+xs7MjLi6OixcvotPpiI+PJz4+nvz8fBITE68rwubvqB6GezmCIHD+/Hl27drFhAkTrsufyMHBgXXr1nHmzBleeuklnnzySZYvX06fPn0A0T/mu+++o0GDBgQEBKBQKGjVqhVRUVEEBARw4sQJxo0bx7Rp0/D19aV3797/+BxvFIVC9BFYtEj0K/jmG9FksHy5GFUyatTNRfndF8IAiB3VQw89xIgRI1iyZAlvvPEGL7zwAjt37uTZZ5/92xStOp2O33//nW3btlG3bl3atGlDTEwMAP3798fLywtHR0fq1q2Li4sLZWVlV9g7DQYD33zzDRqNhnr16knZ8fbu3cv58+dZsWIFLi4uREVF0aBBA/R6Pbt372bQoEGsWLECT09PwsPD8fX1pby8nJ07dzJo0KArwmzuJYS/iupkZWWRnp5Oeno6KSkppKamkp2dTVFREVZWVjg7O+Pi4oKLiwt+fn40b95c+s7V1VVyRLK2tq6h3j958mSNlMOtW7dmzJgxUrKjywd5V1dXxo8fT0xMDF999RXbtm1j1KhR9O/fX65y+B9Aq9WyevVqVqxYQXh4OHPmzKF169b3ZSGl6giCaP89dEj87O4uCgbt298bggBUCbEODg74+vpib2/P8OHDcXZ25syZM8ycOZP27dvj7+9/xwSzzz//nO3bt5OXl4dCoeD48eNotVpefvllli1bdsVkDkThNTY2lmbNmrFz5042bdpEnz59JH+I3377jTlz5kg+OW5ubtL40rRpUyIiIjh37hwnTpy4K8KABRcXMXlccTH88IOYhfKTT6BpUzGN8Y1yX/WednZ2jB8/nhMnTrBt2zZycnJ4++23iYmJuar92GAw8Mcff/D9999Tp04dXnjhBdzd3WsMStHR0ZhMJs6dO8ePP/5IQUEBo0aNuiJRSm5uLllZWVhZWeHo6Ch5d+fk5HDs2DFycnKwtbXF3t5e8uxOT0/nxIkTXLp0iaCgIOzs7HBzc0OtVpOUlERxcfFdEQaEv7zcDQZDjaWgoIDk5GQSExNJTU0lMTGRxMRETCYTGo0GPz8/QkNDiYiIoGvXrkRGRuLn5yfNKK2srGrMLv/Orl+nTh1sbGwICwvjueeeo2/fvjg7O19zYLeysqJjx4488MADbNq0ifnz57N161YmTJhAVFQUdnZ297QvhsyNYdHoHTt2jIULF5Kfn8/UqVPp0KED9vb2970gAPDnn2JCIbNZnO3NmgXduonv7xXUajUBAQGo1Wrat28v5f4wm8189dVX2NjYMHLkSOzs7Pjll19uyT4tzqkODg5XCBiCIJCVlYVer+fgwYMIgkBhYSEmk4kTJ05w8uRJ3n77bWJjY3n11Vf59ttviYiIoGPHjpSVlZGbm4sgCFKGVKPRyDvvvENISAitWrVCqVRSUlLCxx9/TM+ePYmJiaGkpISSkhKsrKzwuR2Zf24AhUKM5psxQ9QqnTwpOhauWSP6FdTijnVN7qFb7e9RKBRER0czZMgQjh8/TkFBgfT91cjPz+eLL76gSZMmdO/eHW9v71rVsEVFRcyYMYO0tDTq1atH+/btMRgMNbatrKzEaDRKKWYtKlmdTkd+fj4mkwmlUlljnVarJT8/H7PZjEqlkhwHraysqKioqBHXevsxA/EkJGSybl0xOTk5ZGRkkJubS2FhISUlJZjNZpydnSUHsj59+hASEiIN+ldTYd8sderUYfbs2Tz++OP4+vped1tKpRIXFxdGjBhB586dWbRoEVOmTCE6Opq+ffsSGxuLh4eHLBTcx5jNZvLy8jh06BBr164lLS2N7t278/TTT+Pq6grc+ZoHtwOdDpYsEWPIAbp3F+sK3G2NgGWmnJCQQFlZGSdOnKBFixb8+OOPzJs3jw4dOuDg4EBERATh4eEUFxezfv16PD09KSgo4Pz586SlpXH48GHy8/P5888/cXJywtfXt8Z+ysvLOXnyJAcPHqSwsJA9e/ZgNBqJjY3lwoULDBs2jNdff53+/fvX+J1SqWTNmjXS57KyMgYMGMDevXvZvHkzeXl5HDlyhKKiIkwmE4WFhaxatYqsrCzi4+P5888/6dixI0899RQAO3bs4I8//uDll1+WJmgqlYqcnByWLl1Ks2bNOHLkCGlpafTp04dHHnnkNv8Df49SCU2awPjxYiIinU4UKseMEWsg3MjjcV8JAyCmek1LS6Nly5b89ttvLF68mOjo6KuGEnl7e/P++++zf/9+fvnlF3755Rd69uxJnTp1aswo3NzcWL16NfHx8Tz55JM899xzfPHFF3Tr1k3axlJHXqhWtMVoNKJWq6UZyuXrrKyspHhnSzSBIAgYjUasra1vyjv/5jEDGzlz5jS//mqHr68vwcHBNG7cGA8PDzw9PXF3d8fd3R0bG5s70tG6ubnxwgsv3NRvLccXGBjI7NmziY+P55dffuGdd97B09OThx56iB49etx0lT+Zu0dmZiabN29m586dlJWV0a1bN15++WXq1KnzrzMHnTsnFqwRBFH1O3y4mBfkbss5giCg1Wrp3LmzlByrZcuWzJkzhwMHDlBYWIhSqcTOzo4WLVowdepUsrOzcXFxYdq0aSQnJ6PX63F3d+eZZ57BysoKXS250E0mEwUFBdjZ2TFp0iQ0Gg0FBQUYjUYCAgKYMWMGjRo1qvUYq/dRNjY2jB8/nkcffRR/f3+CgoL4+OOP8fHxwdHRkYcffphDhw5RWlpKcHAwb775Jk2aNJHqkTg5OTF37ly6d+8utavRaBg+fDinTp2iqKiIyMhI5s6dS9u2bW+45srtQqGAxx8XBcpTpyA7W8xSGBl5gw3dqMfh3YgmsKDVaoWPP/5YmDFjhnDmzBmhSZMmgo2NjTB79mxBr9df87dGo1G4dOmS8PvvvwtTpkwRZs+eLXkf6/V6yQvcZDIJ/fv3F2xtbYXZs2dLXtQWL/nmzZsLvr6+wubNm4XKykrBzc1NqFevnrBz506hTp06QlhYmLBr1y6hpKRE0Gg0QpMmTYR9+/YJ3t7eQv369YWDBw8KOTk5glKpFNq2bSskJSXdiUv3VzSBWYCLwqRJWUJBQYFQWVl5X3tf14ZWqxXOnj0rLFq0SOjYsaPQunVrYd68eUJSUpKg0+lqjRCRubuYzWbBZDIJWq1WOH36tDB9+nShWbNmwkMPPSQsW7ZMSE5OFgwGw90+zNvGypWC4O4u9qlt2wrCuXN3+4hk7jfMZkF4/fWqiLHHHhO/uxHuG2Ob2Wxm69atHDx4kKeffpqoqCjmzZuHu7s7c+fOZe/evddMe6pSqXB3d6ddu3bMnj2bNm3asG3bNvLz85k5cyYnT57EaDSSmppKRkYGNjY2+Pv789577xEVFcXKlSuxtbWlZ8+eaLVaUlNTpcpwDRo0IDY2loceeojS0lIyMjI4evSoVO8+Ojqahx56iIKCArKzszl8+DAqlYqWLVvW6uBy+1AAXtjZ+eLq6opGo/lXqFmrY2NjQ506dXj22WfZsmUL06dP5/jx4wwYMIBnnnmGjRs3cu7cOYqKiu5YmlyZ2rGYASy1BEaNGsUTTzxBXl4e7733Hj/88APDhw8nODj4X6cNsCAIYvSAJcFfRISYn15G5kbp0qVKm3TkSFW56+vlvnjCsrKy+Pnnn3n//fd55JFHsLKykvK9+/r6kpWVxZQpU5g2bRrdunWTys2CWE7266+/rrXjb9CgAVZWVlRWVrJkyRKCgoI4c+YMZWVljB49mgcffJB33nmH4uJisrKyUCgUjBkzhuPHj7Np0ya2bdtG48aNefrpp3Fzc+PZZ58lPj6edevWYTabad++PaNGjcLJyYkXXniBtLQ0Vq1aRWlpKT179mTIkCF/myZW5saxJHPSaDT07t2bbt26ERcXx/bt21m7di3l5eX4+vrSokULGjVqRN26dWWnwzuE8FeK5Li4OI4dOyZlgHNzc6N169bMnDmTyMjIf+3gfzkmk1iZzmQS7b/u7jfu+CUjo1CIlQw1GrGGRVkZVFTcWBKi++KJs7OzIzo6mvfffx9nZ2dsbW1RKBREREQwZ84cTCYTCoUCDw+PK2KebW1tadCgQa3CQEBAAA4ODkyYMIH09HT0ej0tW7bE2dmZqKgoHB0dmTRpEg8//DDR0dGAmDt/8eLFXLhwAZPJRGBgIJF/GWfCwsL45JNPSExMRBAEQkJCCAsLQ6FQUK9ePT799FMpO1ZYWNgdr8T3X8XGxobGjRvTqFEjLl26RFpaGqdOnWLv3r2sWrUKGxsbmjdvTvv27WnatGmNeGVZQLg5Ln/eiouLOXr0KLt372bv3r1UVlYSFhZG165diY2NJTAwUCrw9F9HEO6cv4AgCKxYsYK1a9dK39na2jJu3Di6deuGVqtl06ZN/Pjjj9jY2DBs2DDat29fazGrgoICKfdDUFAQo0ePljIO5ubmsmLFCg4ePEh4eDhjxowhPDyc8vJy1q1bx9atWwkMDGT06NHUqVNHytq6b98+UlNTGTRo0E3dGyaTiS1btvDtt98SFhbGxIkTr5qPZtOmTaxatYro6GhefPHF21oH5XbwTxWd94Uw4OLiQtu2ba/4Pjo6Whqkr/Xbnj17XnObyMhIaUC/nJCQkBpFKRQKBYGBgbU6LCqVyiu2t6BSqQgPDyc8PPyaxyJz+1AqlXh7e+Pt7U3jxo0ZNGgQeXl5HDhwgG3btjFt2jQAGjZsSPv27WncuDHu7u7Y2dlha2t7RY4EmZoIfxVe0mq1lJeXk52dzYEDB9i1axdxcXE4OzvTokULXnjhBR544AGcnZ2xsbG5w0609xYqlVh3QKUStQN5eeLM7k5qBxISEjh27JiUlMre3p7CwkIEQWDDhg188803zJw5k5ycHGbNmsXrr78u1b6wUFFRwYIFC8jNzWX27Nn8+OOPvPTSS3z88cc4Ozvz1ltvATBnzhxWr17N5MmT+eKLL9izZw+bNm1ixowZ/Pzzz7z77ru88847uLq6kp2dzbp165g4ceJNn9uBAwekvDTnz5+XamrURrdu3UhISGDnzp3XjPISBIH8/Px7xoEQREHg4sWqypYODjdeTfi+EAZkZG41lroIQUFBBAUFMWDAACorK4mPj2ffvn1s3bqV5cuXo1Kp8PX1JTIykjp16hAYGIinpyceHh44Ozv/K2Lcbwbhr1obhYWF5OXlkZubS0ZGBomJiaSnp5OTk0NlZSU+Pj40bdqUiRMn0rBhw/tutnW7USjEUrUuLpCfL8aLZ2SIxYjuFO7u7nz//fe0bt26xvd6vZ7PP/+cRx99lObNm1NeXs6vv/7KTz/9RPPmzSUTpyAIZGRksHPnTubMmUOdOnXo378/W7ZsYefOndSrV49Dhw7x0UcfERkZyeDBg9m8eTO//fYbcXFxNGjQgAYNGmA2m5k5cya5ubnY2try448/0rp1awIDA29aCD9z5gxWVlZER0f/bUZEjUZTa62Uy0lLS2PatGk1whrvBXbsqHrfpMmN/14WBmT+lsrKStauXcuhQ4fo27cvBw4c4NKlS/To0UMqdnP8+HF++OEH8vLy8Pf3Z+DAgVI50PsFW1tbGjduzAMPPMDo0aPJzs4mMzOTlJQUEhIS2LJlC4WFhWi1WhwcHPDz8yMiIoLQ0FBCQ0MJCQnB8Z/Uir6HMZvNFBcXk5ycTFJSkpSYyjLoW5JphYeH07lzZ4KDgwkICMDb2xsrK6v75j746aef2LRpExqNhgEDBhAbG8vnn39OSkoKgwcPJjw8nG+//ZYLFy4QGhpK//79CQwM5NSpU3z77bcUFRXh7e3N1KlTr5pG93JiYsDTUxQGTp8WwwwjIkQfgrtJfn4+qamphIaGolAosLW1pX79+qxfv57y8vIa/k4WM6u7uzsKhQIfHx/8/f35/fffsbOzQ6VS4eTkhEKhICAgAC8vL3bv3n3V2fWpU6e4ePHi32YWFQSBzMxMNmzYwLlz53BycqJnz540b96cAwcO8O2333L27FkmTpxI586dGTZsWI3fm0wmNm7cyN69e9HpdCQnJ0sC/vnz51m2bBnFxcW4uroyYsQIBEHgrbfeYu/evUyYMIEnnngCFxcXli5dKm03fPjw21ZZ9WqUlsK6dVWfu3e/8TZuuzAg/JXpTq/XS7Z+y/d6vV5Sx9jY2Ei14y3r9Hq9lC++el356pjNZnQ6HUajUcpjbUnsYzab0Wq1mEwmVCqVVJWq+r4t31dXVRoMBsxm89/mcr/WOVuOX61WS/utDZ1OJ213r3r3W1tbExgYyLx582jTpg0PP/ww3377LVOmTGH//v3Ex8czdepU+vbtS/fu3VmzZg2jR49mw4YNf1sv4l7E0vGFhYURFhZG27Zt0el0VFRUUF5eTklJCcnJycTFxXHixAnWr19Pfn4+KpUKNzc3goODJeEgJCSE0NBQXF1dpfuytlfLfm/X/2+x4Qt/5bmwLJbcF9Urd+bm5pKUlERqaiopKSkkJSWRnp5OcXExZrMZX19fwsPDiYqKol+/fgQFBWFvby8t99PgfznNmjVj/vz5kjbI3t6e0NBQSkpK8Pf3Z9asWeh0OkaOHMk333zDvHnzmDlzJi+++CKDBw+mYcOGrFy5Ep1Od93CQHQ0PPCAmG+gqEisQte1q1it8E5QUVHBtGnTEAQBZ2dnWrduzciRI8nJyQGQ0vJaEn1dunQJvV4v/d4yINvY2Eh9po2NDY6Ojpw9e5bMzEzs7Owkfy5bW1scHR3Jzs6mc+fOrFq1irNnz7Jt2zYCAgKws7NjwYIFTJw4EY1GQ2lpKdbW1leY6Sx+CjNnzsTe3p7Bgwdz5MgRpkyZwvz582nQoAGtWrVCr9fz3HPPXZExUBAEvv76axYvXsyLL75ITEwMq1evJi4uDoDU1FR8fHzo27cvCxYs4JVXXmHp0qX4+/vj7+8vtXnw4EG8vLzo27cvCxcuZMaMGaxbt+6OPQOWktfnz4uffX3F7JU3ym0VBsxmMxcvXuTbb79l27ZtbNq0SRrs09PT+fDDDzl16hQKhYJ27doxduxY3NzcSEhI4KOPPuLcuXMolUq6du3KqFGjakQJgCjV7d+/n88//1xKBTx48GAee+wxFAoFu3fv5osvvpASWowaNYqHHnqIhIQEFixYQGpqKnZ2dowZM4Zu3bqhVqsl1ZiTkxPDhw+/qfPOz89n/vz5nDx5kpCQEGbOnHnV1JXr169n3rx51K9fn48++uiKc7wXUKlUuLq6Ym1tTcOGDalXrx49e/ZkzZo1ZGRksG3bNmxtbRk5ciQODg6EhobSpUsX1q9fL2X3up+xRCZoNBrc3NwQBIF69erRq1cvaZAtLCyUBtC0tDSSk5M5evQoBQUFVFRUoFQqcXJywsnJCVdXV5ycnKRMjy4uLjg4OODo6Ii1tXWtaZ0tWS2rZ7+0IPyVxApqluOunm7aaDSi1+spKSmhtLSUoqIiLl26RElJCcXFxdJSUlIiFYvy9PQkKCiIVq1a8fjjjxMcHEx4eLg0I6x+DPfr4H85np6ejBkzhkWLFpGXl4e7uztlZWU0a9YMhULB9u3bWbx4MR06dJDSox89epSMjAxJcJg/f36twr/ZXFWfvjpWVjBhAmzZIgoDmzeLAsGECXcmHfGTTz7JiBEjcHd35/z587z44oukp6fz6KOPXiGsKpVKjEZjjXLyIJoUqpd9t9ynlslOdUHXsk6v19OtWzfy8vJ4/fXXCQkJYeLEiaxdu5YuXbpw9uxZZs2aRUVFBREREUydOlVKEGQhMTGRI0eO8Nlnn9GiRQtiY2M5evQoS5cu5bPPPsPDwwMHBwdJsKtOZWUln3/+OV27dmXIkCEA7Nu3TxIGunbtSteuXQHRn2Dx4sVShlY7Ozvq/GXL6dKlC126dJG2W7BgAWVlZXdES2g2i/UsFi8Wsw+q1TBihCgQ3OgjedtuNUEQSEtLY+3atWzcuLHGzSMIAkuXLuXixYt89dVX5Ofn88ILL+Dn58fAgQP57LPPMBqNrFixguTkZKZPny5VGKz+kOXk5LBw4ULatWvHyJEj+emnn1i0aBGNGjVCrVazYMEC+vfvT79+/VizZo1Ux+Cnn35CqVSyfPly1q1bx8qVK2natCkeHh4cPHiQ3NxcnnjiiZs+9x07dnDy5Em++uorsrOzr2mHevzxx8nMzOTo0aPXbNNgMLBlyxYefvjhmz6uW4HlgbYMSGVlZWRmZhIYGChpb/z8/HB0dCQxMfGuHuvtorZB0JK5sVmzZtI6s9lMWVkZly5dorCwUBpsLamfi4uLSUpKory8HK1Wi1arvaKTra5Ju9b3ls9ms/mqA7MlW5xGo8He3h4XFxe8vLykglLOzs44Ozvj5uaGh4fHfzLcUqFQ0LJlSz788EOOHDmCp6cn6enpdOzYUarY9/3330v9RHFxMXZ2djz66KPMnz+fzZs307NnTwYMGHCFeruoCJYuBVdX0U8gMFDMLW9jA82bw+DB8PHHojPYvHmiqaBnz9ubllihUODt7S19btCgAaNGjeLdd9+lR48emM1mSXtr0Xja2trW0KQqFAqcnJwwGAyYTCagSmPr5OSEo6OjpG0FcRKn0+lwdnbG0dGRcePGMW7cOMxmMzt27ECv19O8eXOmTJnC448/Ttu2bXnuueekomTVx4Dk5GQcHBxwdXVFoVDg6OiIr68v+/bt+9s8IhcvXqS0tJSGDRtKk9TqJCYm8ttvv0lpjU0mU61tVt/u6NGjV93uViMIon/JvHlVWoH69WHAALiZOlG3Ve50cXGhb9++lJSUsHv3bul7g8HADz/8wLPPPouPjw9ubm60atWKHTt20LFjR3799VdeeeUVqUOKjY1l27Zt9OjRQxpYBUEgJSWFtLQ0KUd2hw4d+OKLL9i2bRshISEUFhbSvn177O3t6d69O5999hm//PILqampREVF4ebmRnR0ND/99BPl5eWYzWZ+//13+vTpc834/8tVrperd5OTk9FoNNLM7/JZguV3wBWdf3UVbvV29+zZw/Lly+nVq5fU3uXb3Y2O2yLlV/fStcxK75UcCncrt5BCocTR0QlHx9qd5gTBLJmrjEZjjYJR4mcjJpM447d0MJZrW72zsZQetry3dNTVNQxVr9ZYWYmfRbXtte+Zu3ft7tZ+RWG2Q4cO/PDDD0RERKBWq/H39yc/Px8HBweaNm0q2YSHDh1KvXr1eOCBB3j44YfZsmULb7zxBq6urvTo0aNG29bWMH++GP/t4gLOzuDhIQ76DRuK1ebq14e4ONEzfOZM0Su8ffvaNQq3Ass9VX327+zsjE6nw9fXF5VKJUUWGI1GcnNz8ff3v8KEGhQUJJnSBEGgrKyM4uJioqKiCAoKoqKiAq1WKxWdKikpoWXLljWOIzMzk/379/PYY49hMpkoKSkhJCQENzc3IiIiyMjIkNK4W1CpVJKZy9KO2Wy+rr7HouGoLZqlpKSEF198kcDAQHr06EFJSQlJSUm1bjdlyhT8/Pzo0aMHpaWlXLhw4fou/j9AEMT76JVXRE2SySSmsB43Dho0uLl75bYJAwqFQippe/nMOCMjg6KiIvz9/aWCPuHh4fzyyy/Ex8dTUVGBt7e3ZLsNDg5m3bp16HS6GsJAYmIiGo0GBwcHFAoFXl5euLu7s2/fPkwmE46OjpKfgq+vL87Ozhw7dgxfX18KCgrQ6XQUFRVJPgNbtmzB19eXBx544JoDq9FoZMeOHSxdupT09HTs7Ox4+OGHGT58OL/88gvLly/n4sWLtG/fnhEjRjBhwoQav8/Ozuatt97ixIkTKBQKioqKpHLKW7duZe7cuZI/w4wZM3BycmLatGmcP3+erl27Mnv2bCorK5kzZ46knps+fToPPfTQLf4X/x47OzuioqJYu3YtBQUFeHl5cerUKcrLy2lyMy6tt4HKSjHLW3n53T6Sy1ECmr+Wm0cQxHrm/wacnCA8/Obqsd8qNBoNXbt2ZcOGDXz11Vc899xzUsU+Ozs7nJ2dadu2LWq1WiqAk5ycTLNmzWjUqBHHjx8nLi7uCmHA3l7MLnjokFh21sKuXeLsX6kUO3WFQvxPT5yAp56CBQtEnwIHBzHkUK2+tYLB888/z9tvv41Go6G8vJxDhw4RHR2Nv78/rVq14tixY/Tp04eioiL2799Phw4dcHR0lBKxeXl54e/vj5eXFxcuXKBBgwacPXuW9PR0xowZg7+/P3Z2diQlJREWFiZVce3Vq5d0DAaDgZ9++ok6depQr149MjIyJH8BSyG4mJiYK7Qt9erVQ6vVkpycTEREBDk5OVy4cIHmzZv/baSPn58fHh4e7Nmzhz59+qBSqSgpKQHEMercuXO8/PLLNGvWjJycHGlMsJhKDAYD6enpxMfHM2XKFFq0aMGlS5du+6TMZBLrD7z8slih0GQSBc1Ro+DJJ8X3N8NdiSbIz8+X7LBQ5bBVXl5OTk4OKpVKkv6USiW2traUlpZeYWooKCiQbKwgzoRsbW3Jzs6msLCwhuOelZUVtra2lJWV0bFjRz777DOWLFnCmTNn6Nq1K2lpacTFxTFq1Ch+/PFHysrKqF+/PjExMVhV65ksQshbb73FkCFDGDx4MPv372f69On4+voyaNAgEhMTOXr0KGvWrLkiCZLJZOK1114jNTWVL774guDgYN59913i4+MB8PLy4p133qF+/fpMnz6dhQsXsmnTJnr06IGnpyc///wzAMePH5fMHv/73//44IMP6NatW41jvVXo9Xri4uIoLCxkx44deHh4cOzYMUpKSvjzzz/p0KEDBw4c4L333iMoKIhDhw7RqVMnyd52t0lMhJEj4W8sMTL3AJ07i85Qd7M6rEKhoGnTpkRHR1NSUkKDBg0AUdM5ZMgQ1qxZQ3p6Or6+voSEhODp6cm8efOkXChKpZKOHTvW2nbduqIwUB2zWVwuRxAgIQH69RNDxZo0EVXAbdrcWtNBfn4+77zzDt7e3uTm5nLixAnGjRuHj48Pzz77LAsXLpTs4Pb29vTu3RtBEJgxYwYajYa5c+fi6+vLiBEj+PHHH8nKyuL8+fO0adOGpk2bYmtry+OPP84333zD2bNnOX36NA899BD169eXBs4DBw6QlZXFiBEjUCqVuLm50bJlSzZs2MCRI0coKSmhdevWNQZ4hUJBUFAQffv2Zc2aNZw9e5a0tDSUSiVDhw4lKyuL48ePk56ezi+//EL79u3x9PSUfm9vb8/48eNZuHAhb731Fn5+fuzfv5/MzExKS0sJCwtj+fLlXLhwgcTERIqKijh+/DgRERHk5+fz+eef06hRIyIjI1mxYgWJiYkkJCRICbYsAuOtxGiEP/6A996DbdtEQUCjEQsVvf76zQsCcJeEgavZQauvq87V7C9X2/ZqKnPLulatWuHg4EBKSgpNmjQhMDCQxYsXM3ToUFatWoXBYMDb25tNmzYxffp0HnjggRrtxMfHYzQa6dixI87OznTs2JGYmBg2bdp0RZnNyyksLOTPP//kmWeeISoqCrPZXKNOd/V91a1bl2PHjtVa6at6Fa/o6GgOHTqETqe7LmEgIQH27IHQUPD2vj4npYiICN5++22cnJxQKpXUrVuX+fPn4+3tTXh4OG+++SYnTpygvLycJ554giZNmtwWwURG5k7g4uJCvXr16NSpU40JxYQJEzh69Ci5ubloNBo8PT0JCAjgmWeeIT8/H6VSyaxZs6TnUxCgpES07aamwqVLN34sej3s2ycmJHrooVtvLnjxxRdJTU1Fp9Ph4+PDgAEDpJTQTZo0YcaMGZw7dw4rKyueeuopQkJCMJvNjBs3DqVSKUWQPProo4SFhZGamiqZTpydnVEoFAwZMoR69eqRmZlJ/fr1adKkSY1+z8XFhbFjx0rqfXt7e8aMGcOxY8coKyvjwQcflLIZVsfe3p4JEyZw/PhxLl68SEREBPXr1yc4OFjSPnTp0uWqOUH69u2Lv78/WVlZ2Nvb8/LLL5OUlERISAhvv/0258+fx8HBQQo5dnR0JDY2lnnz5qHX6/H19WXu3LmcPXtW2q5Ro0a3JZ+G0Sg6lr77rhh5IgiiIDBkCMyaJZqd/gl3RRjw8PDAbDZT+Ve6JEt5TEdHR/z8/DAajZIN2mQyUVFRgYuLyxVOK+7u7mi1WsnBxWAwUFFRIfkhVFZWSg4ter2eyspKIiIi0Gg0NGvWjGbNmqHX61myZAmdOnXCwcGBkydP8sorrxAZGcnJkyc5evQoDRs2rGGnz83Nxd7eXtJs2NjY4OLiwnmLF8c1yM3NRa/XX+EVa7kO69evZ8WKFVy6dInMzEwCaqlaYjab2bRpE8uXLyc3N5fMzEx8fX2v22ll0yaxxKW1tah2DA4WY53r1IGwMIiKEp2cVCpRUFCrrWnRohUtW7aSOqIOHTrUaNOSvOdeR60WQ7auM+pL5g5QUQEFBbXPju80ZWVlaDQacnNzUalUNRxCLabPzp07Y6kPZzSKs7PmzbtiMonq/3PnRO/uuDi4cEHUTOl0YDDcmKlKpRKdC7Va8dqkpMC0aWJb3buL9/A/zUVgKaZ2NZOelZWVlBSo5rGpaNWqVY3v7OzsaNmyZQ1fAAsODg61ZpG1cHn7FvPDgw8++LfH7+rqSqdOna5Y5+vrS9++fa/5eysrqyuOq3nz5tL7hg0b1nqMvXv3vurxX34u/wTLPXb+vKgN+OGHqqJWDg4wcSJMmSL21/9USLwrwoCfnx9ubm5kZGRIHqoXLlwgOjqaunXr4ujoSE5ODoIgUFlZSXJyMrGxsTVU7gqFgrCwMHQ6HaWlpQiCwMWLF8nPz6dbt24EBQVRWlpKZWWl5JxSXFxc4482m83s27cPg8FAx44dSUtLw2g0SjGxTk5OknmiuoONh4cHFRUV0oxdp9NRXFxca4riy7Gzs0OpVFJk+UerkZmZyUsvvcT06dMZMGAAK1euZPXq1bVuN3XqVKZNm8bAgQP55ptvWLFixXVff51OXCwkJorCgQWVSgxNCQ4WPZ6josTa2J6eopOKq6sohTo4iMv95HAeFARffgmXyTIyd5F16+Dpp0WB4G6zdOlSNBoNmZmZtG3bVgoPMxjEgb6kROyMq8/2k5JEASApCXJzReHgn6BQiFq7Bx8UK9F9/z1s3CgKTadOwaBB8NhjMHYstGwpCgX30zMoc32YTJCWBhs2wKJF4r0G4oQmOhpefFEMI7xVzqV3RRiwsrKib9++7N69m969e5Ofn8/+/fsZPXo0Hh4e9OjRg127dtG5c2eSkpI4ffo0zz//PAqFgm+++QZHR0e6d+8uJXX57bffCAsLk3JKd+7cGbVajbu7O7/99huPP/44v/76KzY2NjVmtMXFxWzdupX+/fvj4uJCUVERGo2G5ORk7O3tSUtLo2PHjld4m9arVw+FQsHevXvx8/Pj0KFDxMfHM3369L89dz8/P+rUqcPGjRtp3749NjY2nD59GoDyv6YNltoG2dnZ0mzfxsZGihEvLi6WCiFZtrsR/P1Fb+bMzCopszomk9jRZWRUfadQiOEqHh7g5yeaFzw8RIcoPz9RePD3Fxd39zsTHw1ip2xvf+M2VLnzvHe4l/6LNm3a8PPPv+Pu3gKzuS3ffKMiO1t02MrJEb38s7PFQf96hRcrK/F5CQgQBek9e8SMcbXh5QV9+ogDfrt2omagY0cxyuCLL0TtgE4nOo4dPCgKBQMGiA6G/8ReLHPvIAhi37xpk6gJ2LlT1A6AeD/06ydqBJo2vbVZKm9rnoHDhw+zZMkSDhw4QG5uLoMGDZJicEeNGsWiRYsYMWIEarWahx56iN69e6PRaBg7diyLFy9myJAhWFtb069fPzp16oTBYGDz5s34+vrSqVMnvL29eeGFF/j444955JFHcHFxYcqUKYSEhKBQKJg6dSpLlixh1apVuLm58corr9SIqV29ejX169enQYMGUkjRwIED+eKLL9BqtTRp0oS2bdteEVceHh7O5MmT+frrr1m+fDnW1tYMGDCAzp0788cff/DDDz+QnZ3N9OnTGT9+PBEREdLvra2tefPNN/nf//7H0KFDCQwMxMrKiv3799OrVy86derEjBkziImJISAggOTkZL777jvat2/PN998w7Bhw3jmmWfo1q0b//vf/6hfvz5BQUGkpKSwdu1aRowYcYXT4uU88gg895yosiwoEKXPxMQqtWZqqtjhWFShlqWiQtw2La2qLbVaNDXY2YmDsr29qDUIDha9wkNCRPND/friNhYptro0+08Gg6VLxWOeOLEqheu9NLjI3BtYLGiX39OlpeL9c/48xMdDYmJjkpIaU14uPh9lZeJrLW47wJX3s0YjatPq1xfv+8hIUbNm0aLpdDB8OBw+XLMdlUrMGjd5sugo6OJSdR+7uYmzwM6dRfPD99+LfgRJSbBwoThzbNdOfAYaNqwSjOXn4P7Bcj8WFsJ338Hnn4u+XX8FN6BQiP3bjBmi34in563/fxXCDWZHmDNH9Fo0GOCZZ8SEB7XVTLbEexqNxhqJUVQqlTTTNplMGI1GKU7aksHKEtNqKU1sWQeiX4DlO8u2loQWSqVS+t6SjtgSS3r5OkDat+V4qh8zIB3r5c6IlthcS9vVj7F6kg7LPmvLM2A5ZktWL5PJhFqtluLILcdlNBqlNiztqtVqzGZzrdvVdrwAzz8vdhwA06fDW2/V7CDN5qpXvV7UCpw5I96QliU3VwzTsyzl5VUS6+VYOkfL4KxWi7Oj0FCoV09Uc0VEiN85OIhaB8tiY3N9Eq/ZLIbTrFolailGjYJhw8R2q4dBnzpVFU0QFiaaCa7i7P2PMRqNpKenU1RUJMVvOzs74+/vT2ZmJkVFRVLcutsN5pstLy8nIyODyspKFAoFdnZ2+Pj4SKG1l1NaWkpGRgZ6vR4nJycpMRSIWrHMzEwMBgPOzs4EBgZKMdtFRUVkZWVhMplwdXXF39//thZkWrdOjI8uKLh10QRGo3iPVlRULTk5otB7/nyV4JudLfZl1Z+B2npEpVK8p+ztxXvUzk6c5YeFifeyZeAPC6uy5Ve//y1/T2mpaBL55hvxs5OTOPhPmiSaBWxsrt7JW+zHGzaI9uP4eFFYgSrNXb9+YvKimBjx2ZK1Bfc2ZrNofkpLE01Bq1aJQp6lX1WpRNPmgAFiRkp//9tXs+K25hmoPvDXuvNq6VYv/62VlVWt3uiXz3oteQpqQ6lUXnOWfPm+r+eYLdtdbb/X+/vLf1v9XKu/r36M1RN9qFSqq253vVSfmVe/wWxsxA6uenVoo1HsrLOzIStLVGOlpopq0/x8UVDIyRE9nktKagoXIHa4KSnisnNnVbu2tuLNHhAgDgD+/uJnDw9x8fQUOzV3d/HBqN5RFheL3tkmk3gcb78tPlDDh0OvXuLx3+mABq1Wy+7du1myZAkHDx4kJCSEqVOnMmTIENavX8+iRYvo27cvQ4YM+VthQBAEcnJySE1N5YEHHuDw4cPMnTuX4OBgcnNzSUhI4MEHH+T111+/IslKcXEx8+fP55dffiEgIIBLly4xZcoUHnnkEQoLC5k9ezb79u3D09OT4uJi/ve//9GtWzfy8/OZOXMmZ86cwdHREZ1Ox6xZs67p/HU3sWis8vOr7k+LOj89XRRq09LEezY39/raVKvF+83bW7z/PD1FM5ivr6jpCgioMondqHnKykpsw5J1cMgQePTR6xN+FArx9wMGiL4CP/wg3u/79lUJPl9/LQpXTZuKM8i2bSE2VvT1kTUF9w4mkzjB2rdPzDPx66/ifWtBpRInNV27iiajVq1ubyZKkKsWylwnarVoz/TyElWRIHbEOl2VU1VBgThAFxaKSX4uXBA75PR0UdqtrUR4ZaXofHXuXNV3VlZi5+XkJKpLXV1FVam/v9iRRkSIGgaD4Uqfh/h4UXO1bh307y86Wd1JHBwcGDFiBKGhoQwYMIDy8nIiIiKwsrLCZDLx5JNPMnny5L/NcKnValm3bh2nT5+mXbt2KBQKysvLadOmDZMmTeLChQs8+eSTfPjhhwwaNOiK8qzbt2/ns88+47HHHuOZZ55h+PDhvPnmm7Ro0UIqzzxmzBgGDhzIwIEDmTt3Lg0bNmTdunWsXbuWyZMn06VLF/r27cv8+fOJjo6+40Wnapuhl5eL91JystiZpqSI7y3OfQUF4v1XUXF92RNtbESflzp1xHsqLEw0b7m4iPecs7P43snp1nTGVlai9sPHR4wIiIy88ZmeQiEKzBMmiELvnj1iyNkff4jPRGWl+H7/fvGc6tQRhYdevUSNgWxGuPNY7sXCQnHw37oVjh0TtVSX92H+/jB6dNX/ZTGv3m5kYUDmprHYSDUaUUiwIAhip2QwiBoFo1FUjyYmigJCfDycPSt26iUlVdvp9eKrwSB26pc7aKlUYmdqWZTKKptadXQ60SQQFyeq3fr0EcOz7iQtWrRg8uTJzJo1ixdffJHnnnuOzMxMXn755avWqjCZTBQUFPDnn3+yadMmOnfuzJQpU3BxcUGtVtOlSxc6duyIRqPB0dERe3t7LFU9q2MwGNi9ezcFBQXExMRQp04dPDw8OHr0KLt372bbtm1UVlZSr1496tWrh5OTE+fOnWP//v1s3rwZQRCoU6cODRs2RKPRcOrUKeLi4mjfvv1tv25Go9hhgqj1SUgQ75NTp8TyvllZ4n9puU8Mhtq99xUKUUVe/X5xdBR9WaKixNlyVJQoWNrZ1dzuVmf4q45SKaYXbt/+n+9HqRSFl5AQ0Q/o4EH47DNRECguFp+D8+fFZds2MZNhTIzodNizp6h5uxGznMz1IwjifVlZKfZRx47B+vWiEFBcLH5vuW+VStFUWqcODB0q/j8+PuL9eycFNlkYkLnlWDri6pYQd3ex0/qruBcgPgwFBaK5ITVV7PiTk0V1WXVNg2XWZzKJy/UO7DqdKBCcOXMrz+7vsQzQQ4YM4cCBA2zcuJEPPviA5cuX4+vre5Vj1fHDDz+wd+9eGjRowDvvvIObm1sNXwAbGxsMBgM///wzv/76K6mpqTz77LNS9TQLpaWlXLx4EUEQcHR0xMbGBo1Gg06n48SJE+Tl5QFIuTIsaWhPnjxJQUGBlEjG1tYWGxsb8vPzuXjx4u27YNU4dUq0eWdkiKr/60GlEgd6FxfxPnNxETtTyyzfMuP387v7g55F1X8r21OpxHPu3l18vhISRL+CX38Vhe5Ll6rCiX//XVxeflnU8LVqJWoNwsLEGamX15XmOJnro3qCqfT0KjPA3r2iqepyTZWDg3h/Nmokmoq6dxcF07vFPxIGLE46t9uWIfPP+aexz7cDlarKJmvRcltMD/n5olBgsQVnZooPWU6OaBdOThZniVdzYKxO9YewoEB8QJs0uf05Evz9/enZsyfbtm0jPz+fxMREHnjggVr9O8xmM0VFRahUKpycnK7qdyIIAvn5+ZjNZtzd3SkpKaGgoKCGkGEpXVzdOdbiHFheXn6F46xKpZISf1mSdFkcBi359w212XhuA4WFVZqB2rC2Fm35Ftt9YKD43s1NnOn6+Ij3k5vbnQtvvZdQqUSNx7RpojPtsWNijQPLoGSpiaDVwoED4mJjIwpMluifunXFJTJSvL53W4C6l6moEE1V8fGi5soSmZKYKN7HlwsAarWojWrVStQOPfCAqBG4mSqDt5p/9LisWyeqpOSb5d6nunPKvYzF9GBx0LJg0QhYlpIS+OQTMRnHjVBUJHpi79kD//uf+FDeDoHAUsJ737599OvXj6+//pqFCxfywAMPUKdOnSu8/21tbRk1ahQZGRkcOHCAl156iYceeohu3bpJxbYsjqcjRozg0UcfZdasWXz88cf4+fnx2muvSW1ZWVmh0WikiBdL9IpSqcTV1RVra+sr1qnValxdXSWnVEtlRL1ej1qtvsIUcbuwRJ4olaLnfnCw2FlaMmSGh4v2ezs78T6xvMoz2ZooFFWOjt27i/d9aips3w4//QTHj1eZWHQ6UYNw9qz4O3t7UVB2dBSvd7NmYiW8hg3FNi1FlSyLZX//NiyRJZbaEZYMk2fPigLW4cOi1rGwUDSDlpRcWTDMksXVx0fU2vTtKzo2u7mJ9/G9NHb+I2GgNruujMztQKWqymMA4oN0PVoBi3Ch14sPs52dKJF37SrOiAyGWx9+JQgChYWFfPrppzRr1oxBgwZRUlLCDz/8wLvvvsvChQtrdSC0trYmNDSUkJAQevXqxapVq5gxYwZ9+vShffv25OfnY2Njg5ubG05OTlIK6ri4OIqKikhKSsLb2xsfHx8CAgKk8rOVlZVUVlZia2tLs2bNyMzMRKFQUFxcTHl5OTqdDkdHR5o0acKhQ4eIi4ujpKSEsrIy9Ho9bm5u15Vd81YQHS2WZW3WTHSSqx6adyvyUvzXUCjE+9zbWzQBNG0KL70katp27RKXkyfFSIvCwqrcCmVl4gTiwgXR3GCJOHJ1FbUGMTGigBAWJg5s1UMu7ezE9/eLZsZsFgWi6mGolZXiAJ+aKg7+Z86IJqz09KpQ1NocVK2sRJONh4d4/3boIAoB9evXDDm9F7nhv0utvnYsrMy9z/3ykF4LrbZmhkQLCoUohQcFVamQ1WoxY1tKirhu0qTbl2cAxFn1smXLUCqVDBw4EGdnZ15++WXOnTvHypUr6dixI0OHDq01N4BFA+Dk5MS4cePIyMjg/PnzaLVa1q5di7W1NaNGjSI9PZ19+/YB0K5dOzZt2sTkyZPp3bs3X331FZ07d2bVqlUcO3aMkydPkpubS3R0NC1atJD8E06cOEFkZCRFRUXExsbSuHFjHnvsMXbs2EFcXByenp5UVlbSpk0b6tate/suWDV8fKBTp7tbtfDfSvXcH8HB8MQTYjrb3FxRIDh7tiqnSHKyOBBaojIsM+RLl8Tljz+q2nRyqhI2vLyq3luiMJydxdfq76vnFLldY4klL4NWKw7uZWXi7N0S/VRaWuWTlJdXFR6dmysueXl/P+FQqcR7NTxcFIxCQ0WBtkED8f0dUqjdEm54WBg4ULS33liqIpl7idDQu30E/xydTpzdqFSi6rJhQ/EBjI0VP7u5iYurq9jJbd0qCgO3m/j4eBYtWsSGDRvo3r07hYWFODo6sn//fmmm/corr5CYmCiViQXIyclh+vTptTrqNWjQgKZNm+Li4sJXX33Fli1bpLobr7zyCkOGDGHXrl1SwSwQBYTp06fz5ZdfMnnyZPz9/Xnttddwd3ene/fuTJkyhTVr1nDo0CHq1avHG2+8gaurK3369OHcuXP8/PPPbN++ndatWzNjxozbUoVN5u6jUIiDd7du4qLVioOgZXCMixNnxBYzwuX+HIJQNaBeXqfNyqpq0Ndoar63RG5oNFXZGS3hxNbWVUmeLBEXl4fX6fXiYvExshSMKi0Vl7Iy8bWyUtzOYBC3swgGlZVV77Xa6x/PbGxEQapevSrtSGCgKPx4eIjncC+p/m+EG85AKCNzL1BWJtaFtziPWULCLJ1H9Y7jTmcgtDjiqdVq7OzsUKlUVFZWotPppGyc1tbW2Nra1sjGaSmKdTlqtRoHBweMRiOVlZXSNiqVCo1Gg5WVFUajkYqKCmxsbLCxsUGhUEhVPM1mM1ZWVlKRLKiq4mk2m7G2tsbOzq5GRs+KigoEQaix7nZxOzIQyvxzLOFxFt8CvV5Uk58+LWoSTp0StQcWW7nBUPVqUaVfD9XNQNUH0upmoctvv8vV9Jenm7YkPbteVKqqCCgrK/HVxkb0uYiKEgf+Ro1EvxUHh6q+prb+5n7lX6Awlvkv4uAgqpPvNdRqNc61FBa3s7O7an4BEAd2y6z+alhbW181o6aVldUV+63tu79ry+Kk+Hf1LWT+/VicOaubFd3cRC3c0KHiZ5NJ1CKkp4vRPVlZovkuO7tqhm6p82DxR6ioEGfpFsGh+sB+q6OelMqqgd3WtsrvyMGh6r2joxiS6udX5XTp5ye+/pecU2VhQEZGRkbmplCpRDNDtfpvgDgz12qrnBEtwkBpaZUwoNNV1TipqKjaprr63zLDr6io2b5l9g7iq2WG7uBQs2ianZ0oCFQXBixmCYtA4OAgh8eDLAzIyMjIyNxilMqqyILq2Ukvx2KKsITumUxXVpaEqhonFqqr5qt76FvCHlWqqvcy14csDMjIVCM5OZnk5OQatnuFQkFwcDAODg7Ex8dLiXksZa+jq1d0+guz2UxmZiapqakIgkBoaCj+/v4oFApMJhPp6emkp6ejVCoJCwuTHAktBYgEQSA8PLxGMqHy8nKys7MJDAy8qbh/S0XChIQEtFotDRs2vKpjYFFREWfPnkWr1dK8efNrmjhkZG4US54Lg8GAwWCo8d7yfJnNZsxms1RN1vJMWhJmWfxYLEm8qhe4U6vV0nsrK6sa1WplakcWBmRkqrF69WrefPNNtNVyHms0Gt544w0CAwMZPXo0lZWVgJil76mnnuKTTz6p0YYgCCQmJjJ37lxcXV2l7IIvvfQSdevWJS4ujgULFuDh4YFOp0Ov1zN16lR8fX2ZPXs2RqMRlUqFVqtl3rx5uLu7YzKZ2LNnD2fOnGHMmDE3JQxUVlayfPlyUlJSKCoqYvz48bRo0aLWbUtLS/npp59YunQpu3fvJjw8/Ib3dz+TnZ3Nxo0bqaiooHnz5hw+fBhBEHj00UcJDQ3FbDazc+dOjh49ik6no27dunTv3l2OukAcxMvKyiguLqaoqIiioiKKi4spKSmhsLBQioSpqKigoqICrVaLVqtFp9Nd0ZZlABcEocZgXv2z5b3FOVelUmFra4utrS0ajQZbW1vs7Oywt7fHxcUFZ2dn6bX6+9qq5P6XkIUBGZlqWFlZsWzZMrz/MoKaTCZWrVpFp06dSEhI4H//+x9t2rQBxI7qarUGPv/8cwRB4NVXX8VgMDBx4kS+/fZbpk6dyieffIKzszOvvvoqxcXFPPfcc2zatIm2bdty8OBBvv76a1xdXenZsycHDx6kZ8+eFBYWsm3bNsaMGYODg8NNnZuljRdeeIEGDRqg0Wiuum1gYCB9+vTh22+/vWabJpOJlStX0qNHD+ma/RtwdHSksLCQzz//nK+++oqYmBjee+89EhISWLx4MT/++CPvv/8+w4cPx9HRkU8//ZTTp0/z8ssvX/O63q9UDzqzvDeZTBQWFpKamkpycjKpqanSUlBQgE6nw2g04ujoiJOTkzT4Ojk5YWdnh6+vL87Ozjg4OEiLlZWVFGGjVCql6Jfq7y37FgQBQRAkTYIlEkar1VJaWlpjKS8v59KlS1cIJSaTCRsbGxwcHPDz8yM0NJTg4GCCg4MJCQkhICAAtVp9hWbh36hlkIUBGZlqTJo0qcbDHx8fj6enJ40bNyYhIYG6devS8W/iEvV6PT///DOTJk3CyckJs9lMt27dWL16NY8//jjbt2/ntddew8HBAY1GQ5s2bdi1axeenp6o1Wq8vLxwdHTE2dmZ3Nxc9Ho9n3/+Oe3bt681lXF1zGYzubm5lJSUIAgCzs7OeHh4YDAYSEhIIC0tjYsXLxIcHIzXZcZcQRAoLS3l0qVLGAwG0tPTpY5Wr9eTk5NDRUUFSqUSd3d3nJ2d2bdvHytXrsTPz4/69evj4eFxxXaurq41OvL7AQcHB/z9/bGxsaFly5bY2NiQkJDAmjVruHjxIosXL6ZNmzaMGjUKhUKBs7MzL730Er17976inPT9islkory8nPLyciorKykoKODMmTOcP3+ehIQEkpKSKC8vR61W4+HhQUhICMHBwbRo0YKQkBB8fHzw8vKSnielUim9QlWCreoD7a0YZC1CgkVoqf65utlBEASKi4vJyckhPT2d1NRUkpKSOH78ONnZ2VRWVqJQKPD39yc8PJyoqCjq1auHv78/Dg4OkrbB2tr6XyEcyMKAjEw1qofU6fV6li1bxrBhw6QObP/+/RQXF6NWq/H29qZFixZXhARmZWVRUVGBl5eX1Pn5+/uTnZ1NfHw8BoMBd3d3FAoFarUaX19f0tPT8fb2xmAwkJqairOzMwUFBQQGBvLrr79iNBrp1avX3w6qBw4cYOHChVJtgvLycsaPH09YWBg//PADOTk5fPfdd5w6dYq5c+fW+G1OTg4LFiwgMTERDw8PSkpKqPjLjTslJYV3330XpVJJVlYWnp6evP3222zZsoWTJ0+ybNkyunXrRps2bXj33XdRKBRkZ2fj5ubG4sWLcXR0vAX/zp2n+iCm0Wgwm81kZ2eTl5dH3bp1pVlsvXr1KC0tJTc39y4f8c1jNpvJz88nLS2NzMxMUlJSiI+Pp6CgQMqd4enpSUBAAF26dGHs2LH4+/vj6+uLo6PjPTMg3oh/gEXoa9KkSY3v9Xo9ubm50nVISUnh1KlT7NixA61Wi729PU5OTgQHBxMZGUlQUBABAQH4+fndt8KBLAzIyFyFw4cPY21tLaXirV+/PgUFBVhbW1NSUsLq1atp2LDhFRn6SktLgSrBwhK7r9fryc/Plz5XX6fVagkMDKRHjx68/vrrKJVKevTogbOzMzt27GD48OF88sknnDp1itDQUMaNG3eFEGI0GpkzZw4RERG8+uqrALz99tssWbKEDz/8kIkTJ/LHH38wdepUydRhwWw2s3nzZrZu3cqyZcuoX78++/fv5/DhwwB4eXnx4osvEhoayu7du5kwYQKVlZU8+OCDbN++ndmzZxMaGkpRUREvvPACoaGh/PHHH0yYMIG8vLz7Vhi4Fpfna1OpVLVWpLyXMZlMnD17ln379nH06FFSUlLQ6/V4eHjg5+dHs2bNCAgIwMPDA09PT9zd3aXCWf9mrK2tCQgIICAggBYtWkjmiMLCQvLy8sjLyyM7O5vz58+zfft2Ll68iE6nw8vLi4YNG9KiRQsaN26Mk5PTfXOt7q87V0bmDlFRUcHWrVvp0aOH1MHXq1ePqKgoqaxvVFQUY8eOpXv37nTu3Fn6rWV7i/ezRS2pVCqlioHV7ZyWdRqNhmnTplFUVASI/gvffPMN7dq1Y+fOnfz+++9MnjyZxYsX88477zBnzpwaHU1GRgbnzp1j1KhRuLm5IQgCvXv35umnnyYnJ+eqCYgAtFotZ86cITY2lqioKKysrGrMcCwzqPLycmmmXN3J0sLl2ymVSsnh8u+pxGTSU1GhRqdTo1arpX3dS/j4+ODj48Pp06elapDHjh3D1dX1qj4kdxuLmlyn01FZWcmxY8fYuXMne/fu5dKlS4SFhdGyZUv69+9PVFSU5Hin0WjuOxPP7cCixfP09MTT0xNAquqp1WqprKzk4sWLHDt2jD179rBu3TqMRiNNmjShc+fOtGrVCk9PTzQajWQ2udeQhQEZmcsQBIETJ04gCAJRUVHS9yqVqoZzU6NGjSgrK7tCNezt7Y1KpaL4r+LxliqGzs7OhIWFAVBWVgYgRRq4u7tjY2ODvb099vb2CILA1q1b0ev1tG/fns8//5wOHTrQvHlz+vXrx/z586msrKwR8mdJPWz7V3F0hUIhpSauLc1xdfR6PUVFRbi6ul7RUZlMJrZt28aXX36JyWSioKCA7OzsK2bGJpOJ7du388UXX0izqMzMzOu96sAazp79kZkz3QgJ8ZZCK6t7fDs5OeHg4HDbO9Pi4mKSk5MpLy8nLi4OPz8/zp8/T1FREdnZ2UycOJH33nuPJUuW4OjoyJo1axg8eHCtYaZ3E4tdPDU1lbi4OPbu3UtcXBxqtZr69evz9NNP065duyuEmHtxsLrXsDxfNjY2ODk54e3tTWxsLE888QTl5eWcOHGC33//nW+//ZYlS5bg5+dH69atadq0KSEhIfj5+Un9yb2ALAzIyFyGXq/njz/+oG7duri5uUkd46+//kqHDh0kb/GcnBxsbW1xdXWt8XtXV1fCw8OlfAEmk4kTJ05Qt25dyb6YnJwszdTOnDlDTExMjSiBzMxMfv75ZyZPnoydnZ2kPbDYsC0ahep4e3tjbW1NdnY2IAoaWVlZODs7/62a3sbGBldXV1JSUq5ot7S0lDVr1hAaGsr//vc/zp07x+jRo69oo7S0lLVr1xIUFMTMmTNJSEiodbur0x4PD0+aNs3FYMjjxIkT7Ny5E7PZjE6nw2Qy4eLigpeXF97e3gQGBuLn54e/vz8+Pj631Itfp9MRFhbGpEmTyMvLw8nJidDQUEaPHo1er6dbt244Oztz8uRJysrKeP7552nfvv09YyYQBIH4+Hh27tzJoUOHyMvLw8PDg0aNGjFw4EDq168v+a3I/HMuv44ODg60adOG1q1bU1lZSVJSEidPnuTgwYPs2rULpVJJbGws7du3p3nz5ndEwP077o07V0bmHiIrK4vDhw8zfPjwGirSLVu2YDAYaNeuHfn5+cydO5fGjRvTpEkT1q9fzw8//MCbb75JUFAQTz31FB999BG9e/fGbDazfft2nnvuOdzd3Rk5ciTr1q2jW7du5OXlcfDgQaZPn469vT0gDkQffvgh/fv3JygoCIAWLVpw4MABevfuzS+//EKjRo2uSATk6upKr169WL9+PS1btkSpVPLJJ5/Qrl07AgICyMjIkIQTk8lUQwWv0Who2rQpmzZtYvPmzbRu3ZqdO3dSVlaG2WxGoVCg1+sxmUwkJSVJYVkWX4iMjAwp90H17UpKSjAajVfEiV+JAgjH2zucwYPBy8ssxaFXVlZSVlZGSUkJmZmZJCUlce7cOXbt2kVmZiZGoxEnJycCAgIIDw8nPDycunXrEhUVhYODg3SeN+K17uXlxfDhw2t8d3muhbZt29K2bdtrtnOnqG4GOHLkCCtXruTQoUNERkbSvXt3mjVrhq+vLy4uLveMwPJfQKFQYGdnR0xMDPXr1+fhhx8mNzeXpKQkNm/ezMyZM3F0dGTgwIE8+uijuLi41Ii2uKPHKlctlPm3c6NVC5cuXYpKpWL48OE1HsgNGzbwzTffkJeXh42NDa1bt+bZZ5/Fzc2N9evXs2HDBmbPnk1wcDA6nY5169axdu1aFAoFw4YN47HHHkOtVlNRUcHq1avZsGEDGo2GJ554goceekjqpPfs2UNiYiJDhgyREqEUFhbyzjvvcPz4cSIjI3nllVeuCA0E0fzw4YcfsmvXLgRBoGvXrtLsfOrUqZw+fZrAwEDJ16H6+el0OtauXcuaNWuwsbGhadOm/PbbbzRr1ow2bdqwYsUKTCYT7dq1Y//+/Tg5OTFz5kyWLl3Knj176NGjBw0bNuSzzz7DYDDQvn17Dh48iJ2dHe+///5VfRaut2ph9RCx6lnpMjMzuXDhAikpKSQkJHD27FnJocve3p7g4GDJGSwyMhJ/f3+pcJS9vT22traSOeV+wxJbn52dzfbt2/n2228pKCigU6dOjBw5ksjISClb3/14fv9Wqjsk/vLLL6xatYri4mI6dOhA3759iYqKuuPOh7IwIPOv506WMJa5cW51CWNLBrycnBxSUlLIzMwkJyeH1NRU8vPz0ev1knOik5MTPj4++Pv74+XlhaenJx4eHnh5eeHq6npbZtEWbYeLi8s/shmbzWbOnj3L5s2b+eOPP9BoNHTu3Jnu3bsTHBwsO/7dR2i1Wvbu3cuvv/7KyZMnCQ8Pp2fPnrRt2xZnZ+c7IhTI+iIZGZl/FZZB3snJiTp16gBIKvSSkhJKS0spLi6msLCQ/Px80tPTOXPmDHv27KG4uJiCggJsbW3x9PQkKCiI0NBQIiIipOx0V0tbe70ddlJSEu+99x5NmjRh5MiRkq/I9f5eEATS0tL46quv2LVrF/Xq1WPMmDE0b95cym0hc3+h0Wjo0qULbdq04dy5c2zZsoX333+f5cuXM378eDp27HjbtTuyMCAjI/Ovx5I0SKPR1DCvVC+YYzQaMRqNVFZWkpKSwoULF0hKSmLnzp0sXbqUiooKVCoVAQEBhIaGEhoaSnh4ONHR0bi4uEjhmJbiOFcLi8zKymLz5s2sW7eO5cuXM23aNDp37vy3mRrNZjOlpaX8+OOPfPTRR0RERPD+++9Tt25dbG1tb7kmQBAESkpKKCsrw87ODhcXl5sajARBQKvVSlkx3d3dawhUlnUGgwGFQoGtrS3Ozs7/Sc2GRqMhNjaW6Ohohg0bxnfffceUKVPo0KED48aNIzw8/LaFJsrCgIyMzH+W6pXuquPv718jMZPJZCI/P1/yTcjIyCAxMZFdu3ZRVlaGlZUVjo6OuLm54e3tTVBQED4+Pri5ueHi4oKLiwvu7u5oNBoKCgooLCzEaDRy5MgRhg4dSufOnRkzZgxt2rTBx8fnis6+srKSvXv38vXXX5Obm8sLL7xA7969b2s1SUEQ2LRpE++//z6tWrVi/vz5UtjqjVBWVsbSpUs5fPgwpaWlvPnmm8TExEjrN27cyHfffYeVlZVUtXDSpEl/m/b7coxGI4cPHyYzM5MuXbpckZSroKCAEydOUFhYiEKhwMPDg/r16+Pq6sqlS5c4deoUxcXFKJVKPDw8aNCgAU5OTmRkZHDkyJEahZTc3d3p2rXrDV+L68GSiCwgIIDnn3+eXr168cEHHzB27FgGDBhAv3798PX1veUCgSwMyMjIyPwNKpUKLy8vvLy8JCHB4ptQUFBAfn4+OTk5XLp0iaysLCks0pLkR6lU4unpia+vL4mJiRiNRqltg8HA1q1b2b9/P127dmXgwIE8/PDDklNjeXk5ixcv5rfffqNr16688cYbBAcH3/ZzViqVDBgwgPj4eCkR1s2QkpLC9u3bmTVrFvb29ri7u9dYf+nSJcaNG0e7du0oLS1l6tSpvPHGGzdcOjslJYXRo0eTnp7O3r17rxAGjh07xpIlS2jevDlpaWns3r2bli1b8sEHH/Dnn3/y9ddf06pVK86dO8f+/fvp3Lkz8+bNY9++fUyYMIFLly5JbbVo0eK2CQPVUSgU1KlTh7fffps9e/awbNky9u3bx3PPPUezZs1uaZ4CWRiQ+U9hMkFREdzHKeT/dRQXw9/kRLonqe6bEBISIn1vMBiorKyUMtNVVFSQn59PQkIC586dIz4+/oq2LMmBNmzYwO+//84333zD9OnT8fLy4rXXXuPixYvMnDmTZs2a3VT56luFxQveYDAgCAJqtVrSqljMLIIgoFKpsLKyQhAELl26RGFhISqVipCQkCvyQQwePBhHR0esrKzQaDQ0aNCAgwcPUlZWhkajQafTYWVldU1nToPBwJw5c66Z5Eqn09GtWzdGjBhBVlYW586dY+XKlQwaNAitVsvDDz/MoEGDSEhIYMyYMSxfvpxBgwYBMHr0aAYNGiSZLm6nRqY2nJyc6NmzJw0bNmTp0qVMnjyZKVOm0KdPn1vm5CoLAzL/KVJToV8/kH2s7h0E4f4UBq6GxexgqVdhCdhq06YNRUVFxMXFER8fX2tWSLPZTF5eHps2bWLHjh3Y29vTo0cPvvnmm1qzQ95pysrKWLZsGT/99BMGg4GwsDBeeuklfHx8WLJkCTt27MBgMODh4cFrr72GXq9n7ty5nDx5krFjx9K4cWM++eSTGm1W1xSYzWYyMjIICAjA3d2dkydPMmLECGbNmkW/fv1qPSaj0cjy5csxGAy0atWKvXv31rpdjx49AHG2bcn0aRFgBg4cKK2zVCS0rAPw9PQkJibmruZoUCgU+Pn5MW3aNKKjo3n77bdJS0tj5MiR10w1fr3IwoDMf45/08Ajc+9TfQA3Go0kJSVJgoBCoZCEBTc3N8LCwggMDCQnJ4e0tDSGDh3KM888c08IAoDktzB37lzq1q3LlClTWLFiBS+//DKdO3dm2LBhUl6NP/74g3HjxvHqq68yffp0vvzyyxrpvWsjOTmZ06dPM2nSJMk0M3r0aCkq5HIEQeDUqVP88ccfTJo0iZkzZ161bcusXhAEcnNzSU5OJjQ0lIYNG9ZYl5mZSXp6OpGRkdSvX5/s7GxOnDjBggULJH+CNm3aEBERcZNX8eax+Lj07dsXLy8v5s6dS2lpKRMmTLjCLHKjyMKAzL8epRJsbeEOa/ZkbgKN5t+rtbEUtklLS5PKWjdp0oSYmBjq1auHv78/Hh4epKam8sYbbzBt2jRGjhx5z1R8NBqNxMXF4enpSYMGDfDw8CAmJoZdu3ZhZWVFs2bNACgpKcHX11fKXnk9WMwkq1atolevXpI93s/Pj+eff/6qvyktLeW7776jc+fO1z046/V61q1bR3FxMe+8806N6JLy8nKpyNCcOXNwdXWlbt265Ofn4+/vz8GDB/nkk08IDg7m008/vSIr5Z1CoVDQpk0bZs6cyezZs3FwcODZZ5+9atjr9SALAzL/eurWhS1bRH8BmXsbtfrfLbQ5ODiwfPlyYmNj8fDwkGzhlnCx5ORklixZwiOPPMLTTz/9jzr3W40ltHHfvn10794dtVqNVqslJCSES5cu8dlnn/Hnn39SXFxMUlISDzzwwHW3XVxczNKlS/Hx8WHo0KHX7Rexfft2zp49S79+/UhPT6eiogJBEMjIyCA6OvqK66fVavn666/59ddf+fDDD+nRo4ekcamsrOSzzz7j4MGDfPbZZ1I0Q0xMDNHR0ajVajp16sTRo0f5/fff+emnn5g0adJ1n+OtRqlU0qJFC6ZNm8a0adOIjIykZ8+eNx2SKQsDMv96VCq4RyZXMv9hFAoFzs7OPPbYY7WuLy8v58svv8TLy4tnnnnmnqshoFKpcHd3p23btnz11Vd4eHgA4gx93bp1bNu2jQULFhAREcFzzz13XW0KgkB6ejrff/89vr6+9O/f/7rPWxAEvvjiC7Zs2cKGDRtqrHvsscc4ePAg6enpeHp60rRpU8rLy1m1ahW///47n3zyCY0bN5a2LywsZNmyZRw9epSPP/6YBg0aAKIW4cyZMwQHB+Pq6oqtrS22trZS7oS7jVKppF27djz33HPMmTOHiIiIvzXFXI17626TkZGR+Q8iCAJxcXEcOnSI119/HUdHx3vCR6A6KpWKqKgoli1bxs6dO+nTp49UUbKyshJbW1vc3d0pKiqqEYZ3LSorK/nkk0/YuHEjzZo1Y+vWrYDoPf/UU0/h6OjIZ599xqOPPkrTpk1r/FahUPDcc8/Rv39/QIwWWLJkCRcuXOCVV14hLS2N0aNHExERwZYtW9iyZQtz5syhTp06fPjhh1IbHTt2pLKyknfeeYfY2FgWLFggnW/79u05fPgwHTp0oGfPnhw+fJhz587h4uJCu3btbtWl/UcoFAr69u3L7t27+eqrr3j99ddvqoKnLAzIyMjI3GXMZjPLly+nWbNmNGnS5J4QBEwmE59//jkbN25Er9fzzjvvMHXqVJ555hk+/fRTFixYQFhYGOPGjaNNmzZs3LiRkSNH0qJFC4KDg1m7di316tXjww8/JCkpiZdeeomXXnqpRjIno9HIxYsXyczMrBEW6OnpySOPPILJZGLbtm00a9asVmHAEiEAYqTD9u3bKSws5NFHH0WhUBAeHk5sbCwKhYKUlBTKyso4evQoR48eBarCQzUaDZWVlRw4cEBqz8rKioCAAIKCgli0aBHvvvsuWq2WwMBA5s+fL/lI3AtYW1szfvx4XnzxRU6fPn1T95BcqEhGRkbmLpOTk0OPHj348ssvady48T0hDFiGBstr9WOqPmxc/r3ls+X95dvW1k5tw9Dl7fzdNane1uW/re24a9vP9ay73jLYdxJBECgrK+Pll1+mXr16jBs37obNTLJmQEZGRuYus2/fPtzc3IiJiblnBplrDXpXO8bq31/PoHkj2/wdtbV1+W9vdj/3yn9yNSy5Exo3bszBgwepqKiQ8lxcL/+9ShAyMjIy9xhnzpwhOjr6rmYXlLm/USqVBAQEUFhYWKOOwnX//jYck4yMjIzMDZCfn4+np+fdPgyZ+xwHBwd0Oh2mm4ijls0EMjIyMncZGxubm5rN/VMEQeDw4cOcPXuWyspKHBwcaNSoEVFRUSgUCvbu3cvp06el7a2trenUqRNhYWFXtFVeXs6hQ4dITEzE1dWVVq1aSRUYS0tLOXDgACkpKXh6etK6dWs8PDzQ6/UcO3aM+Ph43NzcaNWqVY0kQElJSRQUFNy0U6XZbObMmTMcPXoUDw8POnTogL29fa3bnjx5ksOHD+Pj40Pnzp1vyiP/bmMwGFCpVDd1rWTNgIyMjMxdJiAggOTk5Luy788//5zk5GRsbGyIi4vj+eef58CBAwiCwNq1a3nmmWekZfLkyRw7duyKNvR6Pd999x1ffvklgiCwc+dO3n77bQoLC9FqtaxYsYKVK1eiUCjYvHkz7777LhUVFRw5coSFCxdSVlbGli1b+OyzzygvLwegtLSUFStWUFFRcdPnduHCBd59913Kyso4cuQI+fn5V91Wo9Fw+PBhPv74Y+kY7jcKCwuxs7O7qWRVsjAgIyMjc5dp1qwZ58+fJ/culNP08PBgypQpDB8+nBdeeIHy8nJ+/fVX9Ho9QUFB/Pzzz6SlpZGWlkZcXBw9e/as8XtBEMjKymLVqlX07duXkSNH8uSTT3LkyBEOHDhAYmIi69at44knnmDkyJGMGjWK3377jUOHDvH7778TGBjIqFGjGDRoEH/++SfZ2dmYTCa2bt2Kt7c3zZs3v+lzO3ToEEVFRQwdOpQXX3wRPz+/q24bGRlJbGzs37ZZVlZ21RTJdxO9Xs+5c+cIDAzE1tb2hn8vmwlkZGRk7jKNGjXCzc2NrVu3MnTo0Dvmva5QKJgzZ4703tbWFgcHB6kUsVqtxtvbm8DAwGu2k5mZSUFBAaGhoahUKiIiIggKCmLz5s10794dnU5HQEAASqWSevXq4eXlxc8//4yVlRUODg5YW1tjb2+PyWRCr9eTmJjIyZMnGTVqFDY2Nle9HoIgUFJSwtmzZ8nLy8PGxobIyEj8/f3JyMhg//79ZGVlsXXrVurUqUPDhg1r/N5sNnP27FnS09MxGo2cP39eWpeXl8fx48fR6XRoNBoaNWqEIAh89dVX7N69m59//plGjRqh0Wg4duyYtF3Dhg2l7Ix3CkEQyM/PZ//+/YwZM0ZOOiQjIyNzP2JlZcWgQYP4+eef6dq1Kz4+Pnds39UHWq1Wy6VLl6hfvz7W1tYUFRXxxRdf8N133+Hs7EyjRo1o164dDg4ONdpIS0vDxsYGW1tbFAoFdnZ2ODk5kZCQQHR0NHZ2dtKg7uDggKOjI6mpqTz44IPs3r2bS5cucerUKZydnXFwcOCrr76idevWBAcHX1Mwqqio4NNPP+X06dNERkaSlZVFYWGhVD65oKCA8vJyyY/hcvbu3ctbb71F3bp18ff3r5H46OjRo6xfv56QkBB27txJs2bNGDduHKdOnaK0tJSkpCTCw8OJj4/nu+++IzQ0lF27dtG4cWPeeuutW/DP3Bg7d+5EoVDQqFGjmxImZWFARkZG5h7gwQcf5Mcff+TXX39l6NChd7w2gdls5vvvv6du3bo8+OCDKJVKevfuTX5+Pi4uLsTFxfH6668zatQoRo0ahbW1NVBVPVCtVktFcpRKJdbW1pSWllJWVoaVldUV6woLC+nWrRunTp1i+PDhuLi4MGbMGP78808cHBxwcXHh6aefJiMjg169ejFixIgaQoggCKSlpbFmzRpef/11HnzwQTIzM3n++edZtWoVr732Gq1ataKkpISJEyde4ThoMBiYP38+Hh4evPbaazg4OPDJJ5/wyy+/ANC8eXMaN26Mi4sLdnZ2rFy5khkzZhATE0NmZiYTJ04EwMfHh4YNG+Lq6oqDgwPLli3jlVdewe4OVdwSBIGcnBw++uj/7d1pTFx128fx77DNDMuwDVT2oewtlGBL2tAmJZGGhIhNrNWUqIktNvGFiolRjKa+qSYgJlb6xBfWmtRgG0mKUgNopHGrlbbYGsFhK9BCAmWZMiyzM+d+Yebc5alPfWJpS8v1SU6GGQ5nhhnC/3eu81/+hxdeeOGWl0JuRcKAEELcYxqNhoSEBPbt28d7773H+vXr7+q0xE6nk9OnT9PR0cEHH3xAVFQUwJIpdx9++GGmp6dpaGhg586dS6oXer2excVFdcliRVHweDzodDp0Oh2Li4tLZgh0u93o9XqSkpKoq6tTf66np4ejR4/y8ssvc+DAAYqKiqiqqqKqqoro6GiefPLJJe+J2WxGq9WSnp5OUFAQJpOJrKwsfvnll7+dUfBG165dY3h4mKqqKsLDw1EUBX9/f/X7Go2GiYkJrl69yszMDG63G4/Hc9NxNBoNk5OTjIyMcP369f9zvztBURQsFgtvvPEGeXl5PPXUU7JqoRBC3M/8/PzYsWMHPT09HDhwgLq6OtatW3fHn9fpdNLW1qae+cfExPxtCAkKCiIuLg6LxbKksfMFGafTqQ6PtNvtzM7OkpqaSkJCAjabTf2ezWZjfn6enJwcALUCYrFYaG1tZdeuXSiKwuTkJAUFBZhMJnJzcxkYGMDj8SzpKe9yudBoNGoDqNFollQobsVms+HxeP52qKHD4eDw4cP09/eTmJhIb2/v34YLu91OTU0NfX19JCYm0tfXpwabu2F2dpb6+nqsVivvvPPObS15LaMJhBBihdDr9ezfv5/8/Hyqq6v5888///EM93YoisKJEye4cuUKzz33HAkJCerji4uLHD16FEVR1LP5sbEx4uPjCQoKUh+Hv4ZGarVapqam1LL16OgoxcXFpKSk4PV6sVqtamn/2rVrbN++XX0dXq+Xn3/+meDgYLUicmMg8Xq9+Pn53RRSUlNTmZ+fV489Pz/P+Pg4GRkZ/1hViY6ORqvVMjAwcNN7fPXqVZqamqisrKSmpoY9e/bcFDAURWF0dJSmpib27t1LTU0NFRUVS6oLd4rv86ivr+fChQtUV1cTFxd3W8eUyoAQQqwgERERvP7669TU1PDKK69w8OBBCgoK7kgfgtHRUaqrq1lYWODgwYPq4yUlJdTW1nLq1CkSEhJIT0/n4sWLfPXVV+zfv5+QkBBeeukldDodb731FomJiZSWlvLZZ58RGRlJa2srERERFBcXYzAY2Lp1K5988gkBAQE0NjaSkpJCUVGR2mCPjo7S1tbG22+/jV6vJzw8nOTkZH744Qfcbjdms5mSkpKbGtq0tDRyc3NpaGggMDCQ8+fP09XVxYcffojL5WJubg6Hw8HU1BSBgYFqPweAqKgoysvLaWxsZMOGDcTGxtLa2orD4UCj0aDT6RgYGCA5OZnu7m7cbjdzc3NER0erHR79/f3V/Uwmk7rf7OwsoaGh/7pkfyterxeLxUJtbS0XLlygtraWgoKC234uWbVQCCFWIKvVypEjR/jyyy/Zt28fjz766LIPWZuYmKC6uvqmiX0KCgqorKzk5MmTdHZ2Mj8/T3R0NGVlZRQXF6PRaDhy5AhBQUHs2bOHkJAQLBYLX3zxBR0dHSQlJVFRUaHOZDg5Ocnnn3/OxYsXWbt2Lc888wwmk0kNAydPniQpKUnto6AoCr29vTQ0NDAxMUFJSQnl5eU3DZnzVRqOHz+O2WwmMjKSnTt3snXrVnp6evjoo4+wWq1kZ2fz9NNP3zRzot1u5+OPP+bSpUsYjUaysrL48ccf2b17NzabTZ3rYOPGjTQ3N1NaWsq2bds4dOgQNpuNiooKJiYmaGlpITY2lk2bNtHc3MyOHTt44oknln2ticXFRTo6Ojh8+DAul4t3332XzMzMZTm2hAEhhFihnE4n3333HfX19aSnp1NVVUVaWtqKX0VPLD+73c6JEyc4fvw4RUVFVFZWkpCQsGx/CxIGhBBiBfNNjHPo0CF+//13XnzxRR5//HG0Wu0dKUOLlcM3KqO7u5u6ujoGBwd58803KS4uJjg4eFlDoYQBIYRY4Xwdxr799lvef/99YmJieP755yksLCQ8PFwqBQ8gp9PJyMgIjY2NNDc3U1RUxGuvvUZsbOwd+bwlDAghxH3C14P92LFjnDlzBpPJxGOPPUZRUREGg+FevzyxDLxeL11dXbS0tHDmzBnWrFnDs88+y+bNm5e9D8KNJAwIIcR9xuFwcPnyZb7++mtaWlpYs2YNe/fuXdLjXqoF9w/fMM2hoSE+/fRT2tvbyc3NZdeuXRQWFhIdHX3HX4OEASGEuE+53W4mJydpamri2LFjhIaGsnv3bkpKSoiLi1v268pi+fj6A8zMzKgjJ3799Veys7N59dVX1fUh7la/EAkDQgjxABgfH+fUqVO0t7czMzNDXl4e27dvJycnh5SUFPz9/SUYrACKonD9+nXMZjPnzp3j7NmzWK1WtmzZQnl5Ofn5+bc1k+C/JWFACCEeEL6Gpru7m59++olz587hcDjIzMzkkUceoaioCKPRKKHgHnC73XR2dvL9999z/vx5FhYWyMjIYNu2bRQWFmIyme7p6BAJA0II8YBRFAWXy8XMzAzDw8O0tLTQ3t6OzWZjy5YtlJWVsWnTJgwGA4GBgQQEBEhAWCaKouD1enG73TidTvr7+zl9+jTffPMN09PTFBQUUFZWxubNm4mMjCQkJGRFDBGVMCCEEA8w3794h8PBb7/9RltbG2fPnsVms5GcnExBQQEbN24kPj4eo9FIVFTUXV8++X7n9XqZn59nenqa8fFxent76ezsxGw2Y7PZyMrKoqSkhNLS0iWdAVdSAJMwIIQQq4hvQZ/e3l7MZjN//PEHQ0NDuN1udDoda9euJS0tjYyMDDIzM4mNjV0RZ64rie89HBoaor+/n97eXgYHB5menkaj0WA0GsnJySE3N5d169bx0EMP3ZUFjG6HhAEhhFilFEXB6XQyOTnJ9PQ0o6OjdHZ20tfXx+XLl1EUhYSEBLKzs8nNzWX9+vWkpaWh1+vVs9r/fXu/8zWJN94qisLIyAhms5menh66uroYGBhgYWGBuLg4srKyyMvLIzMzk5iYGGJiYggLC1vxAeBGEgaEEEIA/126eHFxEY/HQ09PD5cuXaK7u5vu7m7Gx8cBiI+PJz09naSkJFJSUkhNTSU8PBytVotOp0Or1aqrBAYEBKy4yoLv93S73bhcLlwuF06nE4fDwcLCAmNjYwwNDXHlyhWGh4cZGhrCZrNhMBhIS0sjLy+P/Px8NmzYQEREBP7+/upojfs1FEkYEEII8Y98S+f6GsfBwUHGxsaYmppiZmYGRVHUMKDX6wkLCyM6OprIyEgMBgMhISEYDAZCQ0PV/XzB4cb7/3YIpNfrVRt0p9O55GuHw4HNZmNubo75+XlmZ2exWq1YLBYsFgt2u13dz+PxEBwcTFRUFHFxcSQnJ2MymUhLSyM+Pn7JMsgPEgkDQggh/hWv14vNZsNqtWK1WtVG1mq1MjMzo15+WFhYwG63s7CwgMPhUM/GFxcX1QpCUFAQWq1WDQM6nQ4/Pz/8/PzQ6/VLntd3Rg9/zeHv8XjUERQ3bl6vVz22L6QEBwcTHBxMREQERqMRo9FIeHg4BoNBvfVtq2mUhYQBIYQQy8o3vM53ueHGSw9er1fdXC4Xs7Oz6ma1WnE6nXi9Xux2u1rOt9vtS47vCxAajUZt7DUaDREREYSFhakNe3BwsBoo/Pz8CAgIUEv6/v7+6v3V0uDfioQBIYQQ98Sdbn6kkf//k8GkQggh7glprFeOldXFUwghhBB3nYQBIYQQYpWTMCCEEEKschIGhBBCiFVOwoAQQgixykkYEEIIIVY5CQNCCCHEKidhQAghhFjlJAwIIYQQq5yEASGEEGKVkzAghBBCrHL/AQ7XOhf190E2AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([2, 4, np.NaN, np.NaN], N / 4),\n",
    "    'y': np.repeat([2, 4, 1, 10], N / 4)\n",
    "\n",
    "})\n",
    "\n",
    "lgbm_params = {\"verbose\": -1}\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMRegressor, lgbm_params)\n",
    "display(predict_and_evaluate_regressor(df, [\"X\"], \"y\", model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c4a2658-4d5c-484c-921f-c8b80b481fe4",
   "metadata": {},
   "source": [
    "## NaN not on training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a291314b-8833-4618-9b0d-5f29b297dab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSe is: 1.3280699750817249e-05\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "   node_depth node_index  split_gain  threshold decision_type  \\\n",
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       "LGBMRegressor(verbose=-1)"
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([1, 2], N / 3),\n",
    "    'y': np.repeat([1, 2], N / 3)\n",
    "\n",
    "})\n",
    "\n",
    "lgbm_params = {\"verbose\": -1,  \"n_estimators\": 100}\n",
    "\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMRegressor, lgbm_params)\n",
    "display(predict_and_evaluate_regressor(df, [\"X\"], \"y\", model))\n",
    "display(predict_missing_value(model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "89de2603-dcd2-42f7-a235-eb535b254721",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSe is: 1.3280699750817249e-05\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.999987</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000013</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   X  y    y_pred  count\n",
       "0  1  2  1.999987    333\n",
       "1  2  1  1.000013    333"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>y_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.999987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    X    y_pred\n",
       "0 NaN  1.999987"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>node_depth</th>\n",
       "      <th>node_index</th>\n",
       "      <th>split_gain</th>\n",
       "      <th>threshold</th>\n",
       "      <th>decision_type</th>\n",
       "      <th>missing_direction</th>\n",
       "      <th>value</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0-S0</td>\n",
       "      <td>166.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>left</td>\n",
       "      <td>1.50</td>\n",
       "      <td>666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.55</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0-L1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>1.45</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   node_depth node_index  split_gain  threshold decision_type  \\\n",
       "0           1       0-S0       166.5        1.5            <=   \n",
       "1           2       0-L0         NaN        NaN          None   \n",
       "2           2       0-L1         NaN        NaN          None   \n",
       "\n",
       "  missing_direction  value  count  \n",
       "0              left   1.50    666  \n",
       "1              None   1.55    333  \n",
       "2              None   1.45    333  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "LGBMRegressor(verbose=-1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 1000\n",
    "df = pd.DataFrame({\n",
    "    'X': np.repeat([1, 2], N / 3),\n",
    "    'y': np.repeat([2, 1], N / 3)\n",
    "\n",
    "})\n",
    "\n",
    "lgbm_params = {\"verbose\": -1,  \"n_estimators\": 100}\n",
    "\n",
    "\n",
    "model = train_lgbm_model(df[[\"X\"]],df[[\"y\"]], lgb.LGBMRegressor, lgbm_params)\n",
    "display(predict_and_evaluate_regressor(df, [\"X\"], \"y\", model))\n",
    "display(predict_missing_value(model))\n",
    "display(trees_to_dataframe(df, tree_indexes_to_filter=[0]))\n",
    "plot_tree_on_diagram(model, n_trees_to_plot=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61d032e7-eb80-4388-8e26-3924b242a91c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}